Spectroscopic analysis

ABSTRACT

A method and analyzer for identifying or verifying or otherwise characterizing a sample comprising: using or having an electromagnetic radiation source for emitting electromagnetic radiation in at least one beam at a sample, the electromagnetic radiation comprising at least two different wavelengths, using or having a sample detector that detects affected electromagnetic radiation resulting from the emitted electromagnetic radiation affected by the sample and provides output representing the detected affected radiation, and using or having a processor for determining sample coefficients from the output, and identifying or verifying or otherwise characterizing the sample using the sample coefficients and training coefficients determined from training samples, wherein the coefficients reduce sensitivity to a sample retainer variation and/or are independent of concentration.

FIELD OF THE INVENTION

The present invention relates to a spectroscopic analyser, such as aspectrophotometer, for verifying and/or identifying or otherwiseanalysing drugs, blood or other substances.

BACKGROUND OF THE INVENTION

Spectroscopy, for example through the use of a spectroscopic analysersuch as a spectrophotometer, can be used to analyse substances. Forexample, by directing incident radiation towards a sample, and analysingthe spectral nature of the affected radiation, it can be possible togain an indication of the nature of the sample.

However, such analysers often provide inaccurate analysis. Accuratelydiscriminating between different substances can be difficult.

SUMMARY OF INVENTION

It is an object of the present invention to provide an analyser and/ormethod for verifying or identifying or otherwise characterising a drugor other substances using spectroscopy.

The embodiments described in the present specification are directedtowards drug characterisation but the invention is not limited to justcharacterising drugs. Those skilled in the art will appreciate that thedisclosure herein can be applied to characterisation of other substancesalso.

In one aspect the present invention may be said to consist in ananalyser for identifying or verifying or otherwise characterising asample comprising: an electromagnetic radiation source for emittingelectromagnetic radiation in at least one beam at a sample, theelectromagnetic radiation comprising at least two different wavelengths,a sample detector that detects affected electromagnetic radiationresulting from the emitted electromagnetic radiation affected by thesample and provides output representing the detected affected radiation,and a processor for determining sample coefficients from the output, andidentifying or verifying or otherwise characterising the sample usingthe sample coefficients and training coefficients determined fromtraining samples.

Preferably the sample detector output represents intensities detected bythe detector at the at least two wavelengths, and wherein determiningthe sample coefficients comprises determining and using a fractionalspectral intensity at each wavelength.

Preferably the analyser further comprises a reference detector fordetecting reference electromagnetic radiation at the at least twowavelengths that provides output representing intensities detected atthe at least two wavelengths, and the fractional spectral intensity ateach wavelength is a normalised fractional spectral intensity using theoutput from the reference detector.

Preferably the analyser is used on a plurality of training samples toobtain from the sample detector training output for a plurality oftraining samples representing intensities detected by the detector atthe at least two wavelengths, and wherein the processor is configured todetermine the training coefficients by determining and using afractional spectral intensity at each wavelength of the training output.

Preferably the fractional spectral intensity is a normalised fractionalspectral intensity using output from a reference detector.

Preferably the fractional intensity is defined as the proportion oftransmitted light measured at a wavelength referenced to the sum ofintensities over all the at least two wavelengths.

Preferably the normalised spectral intensity at each wavelength isdetermined in the processor using:

$g_{m} = \frac{f_{m}}{\Sigma\; f_{m}}$Where: f_(m) is an electromagnetic radiation intensity (or someparameter related to it—processed or unprocessed) detected at thewavelength, and preferably the intensity f_(m) is a ratio of (optionallyan average of) sample intensity(ies) to (optionally an average of)reference intensity(ies) or of (optionally an average of) trainingintensity(ies) to (optionally an average of) reference intensity(ies) asappropriate, andΣf_(m) is the sum of intensities over all of the at least twowavelengths.

Preferably the sample and/or training samples comprise a substance in adilutant with a concentration and the sample coefficients and/ortraining coefficients are independent of the concentration.

Preferably each sample coefficient is determined by:

$y_{m}^{B} = {\frac{s_{m}^{B}}{\sqrt{{\Sigma_{m}( s_{m}^{B} )}^{2}}} \equiv \frac{{g_{m}^{B}(x)} - \overset{\_}{g_{m}^{0}}}{{\Sigma_{m}( {{g_{m}^{B}(x)} - \overset{\_}{g_{m}^{0}}} )}^{2}}}$Where g_(m) ^(B)(x)−g_(m) ⁰ =s_(m) ^(B)x (preferably being a slope ordifference between the undiluted substance and a dilutant)X is the concentration of the substance in the dilutantB denoting blind test sampleg_(m) ^(B)(x) is the fractional spectral intensity of the sample withunknown concentration, andg_(m) ⁰ is the fractional spectral intensity of the dilutant.

Preferably each training coefficient is determined by:

$y_{m} = \frac{s_{m}}{\sqrt{\Sigma_{m}s_{m}^{2}}}$where s_(m)=g_(m) −g_(m) ⁰ (preferably being a slope or differencebetween the undiluted substance and a dilutant)g_(m) is the fractional spectral intensity of the undiluted sample(being the undiluted substance), andg_(m) ⁰ is the fractional spectral intensity of the dilutant.

Preferably the analyser further comprises a modulator such that theemitted electromagnetic radiation at the sample is modulatedelectromagnetic radiation and prior to determining the samplecoefficients the processor extracts the desired spectral component fromthe intensity at each of the at least two wavelengths to eliminate thedark current.

Preferably prior to determining the training coefficients the desiredspectral components are extracted by the processor from the intensity ateach of the at least two wavelengths to eliminate the dark current.

Preferably the processor extracts the desired spectral component bymultiplying the output representing the detected affected modulatedelectromagnetic radiation by sine and cosine functions and integratingover the period of modulation oscillation to remove the dark currentcomponent.

Preferably the processor extracts the desired spectral component byconducting a Fourier Transform on the output representing the modulateddetected affected radiation and removing the dark current component fromthe transformed output.

Preferably the analyser further comprises a temperature sensor tomeasure the temperature of the sample and provide temperature output tothe processor, wherein the processor corrects the desired spectralcomponents of the training coefficients at the at least two wavelengthsto the temperate of the sample.

Preferably the temperature is corrected according to:

$\begin{matrix}{{I( T_{t} )} = {{I( T_{b} )} + {\frac{dI}{dT}\Delta\; T}}} & (1)\end{matrix}$Where,I is the intensity of affected electromagnetic radiation detected by adetector at a particular wavelength for a sample,T_(t) is the temperature of the training sample when the affectedelectromagnetic radiation was detected at that wavelength,T_(b) is the temperature of the unknown sample when the affectedelectromagnetic radiation was detected at that wavelength,ΔT=T_(t)−T_(b) is the sample temperature difference between the trainingsample temperature and unknown sample temperature, and

$\frac{dI}{dT}$is the slope or the linear relationship of between measure intensity andtemperature for a sample at a given wavelength.

Preferably to identify or verify or otherwise characterise the sampleusing the coefficients and training coefficients determined fromtraining samples, the processor: determines or obtains a training valuefor each training sample based on a combination of weights for eachtraining coefficient for each of the training samples, determines orobtains a sample value for the sample based on a combination of weightsfor each sample coefficient, identifies or verifies or otherwisecharacterises the sample based on the relationship between the trainingand sample values.

Preferably further comprising the processor determining theconcentration of the sample.

Preferably to determine the concentration of the sample the processoruses:

$x = \frac{{g_{m}^{B}(x)} - \overset{\_}{g_{m}^{0}}}{s_{m}}$Where x is the concentration, andg _(m) ^(B)(x)− g _(m) ⁰ =s _(m) ^(B) xX is the concentration of the substance in the dilutantB denoting blind test sampleg_(m) ^(B)(x), is the fractional spectral intensity of the sample withunknown concentration, andg_(m) ⁰ is the fractional spectral intensity of the dilutants _(m)= g _(m) − g _(m) ⁰g_(m) is the fractional spectral intensity of the undiluted sample(being the undiluted substance), andg_(m) ⁰ is the fractional spectral intensity of the dilutant.

Preferably each wavelength or at least two of the wavelengths is betweensubstantially 1300 nm and 2000 nm, and each wavelength or at least twoof the wavelengths is in the vicinity of the wavelength(s) of (or withina region spanning) a spectral characteristic in the liquid spectrumbetween substantially 1300 nm and 2000 nm.

Preferably the electromagnetic radiation comprises a plurality ofelectromagnetic radiation beams, each beam having a differentwavelength.

Preferably the source is a laser comprising a photodetector, wherein thephotodetector is the reference detector.

Preferably the liquid is water, there are six electromagnetic radiationbeams and the wavelengths are substantially 1350 nm, 1450 nm, 1550, nm,1650, nm, 1750 nm and 1850 nm, and optionally wherein 1450 nm is theanchor wavelength.

Preferably the sample is in an intravenous delivery device such as an IVinfusions set or syringe, or other receptacle such as a test-cell,test-tube, flow cell or the like.

In another aspect the present invention may be said to consist in amethod for identifying or verifying or otherwise characterising a samplecomprising: emitting electromagnetic radiation in at least one beam at asample, the electromagnetic radiation comprising at least two differentwavelengths, detecting affected electromagnetic radiation resulting fromthe emitted electromagnetic radiation affected by the sample andproviding detected output representing the detected affected radiation,determining sample coefficients from the output, and identifying orverifying or otherwise characterising the sample using the samplecoefficients and training coefficients determined from training samples.

Preferably the detected output represents intensities detected at the atleast two wavelengths, and wherein determining the sample coefficientscomprises determining and using a fractional spectral intensity at eachwavelength.

Preferably the method further comprises detecting referenceelectromagnetic radiation at the at least two wavelengths and providingoutput representing intensities detected at the at least twowavelengths, and the fractional spectral intensity at each wavelength isa normalised fractional spectral intensity using the output from thereference detector.

Preferably the method further comprises: for a plurality of trainingsamples, emitting electromagnetic radiation in at least one beam at eachtraining sample, the electromagnetic radiation comprising at least twodifferent wavelengths, for each training sample, detecting affectedelectromagnetic radiation resulting from the emitted electromagneticradiation affected by the sample and providing detected outputrepresenting the detected affected radiation, for each sample,determining training coefficients from the output by determining andusing a fractional spectral intensity at each wavelength of the trainingoutput.

Preferably the fractional spectral intensity is a normalised fractionalspectral intensity using output from a reference detector.

Preferably the fractional intensity is defined as the proportion oftransmitted light measured at a wavelength referenced to the sum ofintensities over all the at least two wavelengths.

Preferably the normalised spectral intensity at each wavelength isdetermined by:

$g_{m} = \frac{f_{m}}{\Sigma\; f_{m}}$Where: f_(m) is an electromagnetic radiation intensity (or someparameter related to it—processed or unprocessed) detected at the m^(th)wavelength, and preferably the intensity f_(m) is a ratio of (optionallyan average of) sample intensity(ies) to (optionally an average of)reference intensity(ies) or of (optionally an average of) trainingintensity(ies) to (optionally an average of) reference intensity(ies) asappropriate, andΣf_(m) is the sum of intensities over all of the at least twowavelengths.

Preferably the sample and/or training samples comprise a substance in adilutant with a concentration and the sample coefficients and/ortraining coefficients are independent of the concentration.

Preferably each sample coefficient is determined by:

$y_{m}^{B} = {\frac{s_{m}^{B}}{\sqrt{{\Sigma_{m}( s_{m}^{B} )}^{2}}} \equiv \frac{{g_{m}^{B}(x)} - \overset{\_}{g_{m}^{0}}}{{\Sigma_{m}( {{g_{m}^{B}(x)} - \overset{\_}{g_{m}^{0}}} )}^{2}}}$Where g_(m) ^(B)(x)−g_(m) ⁰ =s_(m) ^(B)x (preferably being a slope ordifference between the undiluted substance and a dilutant)X is the concentration of the substance in the dilutantB denoting blind test sampleg_(m) ^(B)(x), is the fractional spectral intensity of the sample withunknown concentration, andg_(m) ⁰ is the fractional spectral intensity of the dilutant.

Preferably each training coefficient is determined by:

$y_{m} = \frac{s_{m}}{\sqrt{\sum_{m}s_{m}^{2}}}$where s_(m)=g_(m) −g_(m) ⁰ (preferably being a slope or differencebetween the undiluted substance and a dilutant)g_(m) is the fractional spectral intensity of the undiluted sample(being the undiluted substance), andg_(m) ⁰ is the fractional spectral intensity of the dilutant.

Preferably the emitted electromagnetic radiation at the sample ismodulated electromagnetic radiation and prior to determining the samplecoefficients the desired spectral component is extracted from theintensity at each of the at least two wavelengths to eliminate the darkcurrent.

Preferably prior to determining the training coefficients the desiredspectral components are extracted from the intensity at each of the atleast two wavelengths to eliminate the dark current.

Preferably the desired spectral component is extracted by multiplyingthe output representing the detected affected modulated electromagneticradiation by sine and cosine functions and integrating over the periodof modulation oscillation to remove the dark current component.

Preferably the desired spectral component is extracted by conducting aFourier Transform on the output representing the modulated detectedaffected radiation and removing the dark current component from thetransformed output.

Preferably the method further comprises measuring the temperature of thesample and provide temperature output to the processor, and correctingthe desired spectral components of the training coefficients at the atleast two wavelengths to the temperate of the sample.

Preferably the temperature is corrected according to:

$\begin{matrix}{{I( T_{t} )} = {{I( T_{b} )} + {\frac{dI}{dT}\Delta\; T}}} & (1)\end{matrix}$Where,I is the intensity of affected electromagnetic radiation detected by adetector at a particular wavelength for a sample,T_(t) is the temperature of the training sample when the affectedelectromagnetic radiation was detected at that wavelength,T_(b) is the temperature of the unknown sample when the affectedelectromagnetic radiation was detected at that wavelength,ΔT=T_(t)−T_(b) is the sample temperature difference between the trainingsample temperature and unknown sample temperature, and

$\frac{dI}{dT}$is the slope of the linear relationship of between measure intensity andtemperature for a sample at a given wavelength.

Preferably to identify or verify or otherwise characterise the sampleusing the coefficients and training coefficients determined fromtraining samples, comprises: determining or obtaining a training valuefor each training sample based on a combination of weights for eachtraining coefficient for each of the training samples, determining orobtaining a sample value for the sample based on a combination ofweights for each sample coefficient, identifying or verifying orotherwise characterising the sample based on the relationship betweenthe training and sample values.

Preferably the method further comprises determining the concentration ofthe sample.

Preferably determining the concentration of the sample the processoruses:

$\begin{matrix}{x = \frac{{g_{m}^{B}(x)} - \overset{\_}{g_{m}^{0}}}{s_{m}}} & (7)\end{matrix}$Where x is the concentration, andWhere g_(m) ^(B)(x)−g_(m) ⁰ =s_(m) ^(B)xX is the concentration of the substance in the dilutantB denoting blind test sampleg_(m) ^(B)(x), is the fractional spectral intensity of the sample withunknown concentration, andg_(m) ⁰ is the fractional spectral intensity of the dilutants _(m)= g _(m) − g _(m) ⁰g_(m) is the fractional spectral intensity of the undiluted sample(being the undiluted substance), andg_(m) ⁰ is the fractional spectral intensity of the dilutant.

Preferably each wavelength or at least two of the wavelengths is betweensubstantially 1300 nm and 2000 nm, and each wavelength or at least twoof the wavelengths is in the vicinity of the wavelength(s) of (or withina region spanning) a spectral characteristic in the liquid spectrumbetween substantially 1300 nm and 2000 nm.

Preferably the electromagnetic radiation comprises a plurality ofelectromagnetic radiation beams, each beam having a differentwavelength.

Preferably the liquid is water, there are six electromagnetic radiationbeams and the wavelengths are substantially 1350 nm, 1450 nm, 1550, nm,1650, nm, 1750 nm and 1850 nm, and optionally wherein 1450 nm is theanchor wavelength.

Preferably the sample is in an intravenous delivery device such as an IVinfusions set or syringe, or other receptacle such as a test-cell,test-tube, flow cell or the like.

In another aspect the present invention may be said to consist in amethod for identifying or verifying or otherwise characterising a samplecomprising: emitting electromagnetic radiation in at least one beam at asample, the electromagnetic radiation comprising at least two differentwavelengths, detecting the emitted electromagnetic radiation at eachwavelength and providing detected output representing the emittedelectromagnetic radiation being reference intensity detected at eachwavelength, detecting affected electromagnetic radiation resulting fromthe emitted electromagnetic radiation affected by the sample andproviding detected output representing the detected affected radiationbeing output intensity detected at each wavelength, measuring thetemperature of the sample determining sample coefficients from theoutput, and identifying or verifying or otherwise characterising thesample using the sample coefficients and training coefficientsdetermined from training samples, wherein determining the samplecoefficients comprises: eliminating dark current from the output of thereference and output intensities, determining fractional spectralintensities from the reference and output intensities, determining aconcentration independent coefficient from the fractional spectralintensities, wherein the training coefficients have be determined fromdata temperature corrected to the temperature of the sample.

In another aspect the present invention may be said to consist in ananalyser for identifying or verifying or otherwise characterising asample comprising: an electromagnetic radiation source for emittingelectromagnetic radiation in at least one beam at a sample, theelectromagnetic radiation comprising at least two different wavelengths,a sample detector that detects affected electromagnetic radiationresulting from the emitted electromagnetic radiation affected by thesample and provides output representing the detected affected radiation,and a processor for determining sample coefficients from the output, andidentifying or verifying or otherwise characterising the sample usingthe sample coefficients and training coefficients determined fromtraining samples, wherein the sample coefficients are found from theslope/difference of normalised spectral intensity at a particularwavelength normalised with respect to the root-sum-of-squaresslope/difference of normalised spectral intensity taken over allwavelengths, each slope/difference of normalised spectral intensitybeing obtained from detector output for the sample in undiluted form anda dilutant for a particular wavelength, and each normalised spectralintensity being found from the detected intensity at a particularwavelength over the sum of detected intensities for all wavelengths fora sample.

The wavelengths relate to the test wavelengths.

In another aspect the present invention may be said to consist in ananalyser for identifying or verifying a liquid based drug samplecomprising: an electromagnetic radiation source for emittingelectromagnetic radiation in at least one beam at a sample, theelectromagnetic radiation comprising at least two different wavelengths,a sample detector that detects affected electromagnetic radiationresulting from the emitted electromagnetic radiation affected by thesample and provides output representing the detected affected radiation,and a processor for identifying or verifying the sample from thedetector output representing the detected affected electromagneticradiation, wherein each wavelength or at least two of the wavelengths isbetween substantially 1300 nm and 2000 nm, and each wavelength or atleast two of the wavelengths is in the vicinity of the wavelength(s) of(or within a region spanning) a spectral characteristic in the liquidspectrum between substantially 1300 nm and 2000 nm.

Preferably the electromagnetic radiation comprises a plurality ofelectromagnetic radiation beams, each beam having a differentwavelength.

Preferably verifying or identifying the drug sample is againstcomparison data for one of a set of n drugs, and wherein theelectromagnetic radiation comprises at least log₂n different wavelengthsin one or more beams.

Preferably the different wavelengths span or capture a plurality of atleast some of the spectral characteristics in the liquid spectrumbetween 1300 nm and 2000 nm.

Preferably the liquid spectrum comprises two or more spectralcharacteristics, and wherein: each spectral characteristic falls in orspans a region of the liquid spectrum, each wavelength falls within oneof the regions.

Preferably each region is defined by a wavelength range.

Preferably the spectral characteristics comprise peaks, troughs,inflections, stable points or regions plateaus, knees and/or slopes ofthe liquid spectrum.

Preferably the liquid is water and comprises spectral characteristicsfalling in the following regions of the water spectrum: a first regionbetween 1300 nm and 1400 nm, a second region between 1400 nm and 1500nm, a third region between 1500 nm and 1600 nm, a fourth region between1600 nm and 1700 nm, a fifth region between 1700 nm and 1800 nm, and asixth region between 1800 nm and 200 nm.

Preferably the electromagnetic radiation has an anchor wavelength in thevicinity of the wavelength(s) of (or within a region spanning) a stableregion in the liquid spectrum.

Preferably the each wavelength further corresponds to a wavelengthproduced by a source that is readily/cheaply obtainable.

Preferably the source is a plurality of lasers, each laser configured toemit an electromagnetic radiation beam at a fixed or tuneablewavelength.

Preferably comprises a modulator for modulating the electromagneticradiation beam(s) emitted at the sample resulting in detected affectedradiation detected by the sample detector that is modulated wherein theprocessor as part of identifying or verifying the sample from the outputfrom the detector removes the dark current component from the outputrepresenting the detected affected modulated electromagnetic radiation

Optionally the processor removes the dark current component bymultiplying the output representing the detected affected modulatedelectromagnetic radiation by sine and cosine functions and integratingover the period of modulation oscillation to remove the dark currentcomponent.

Optionally the processor removes the dark current component byconducting a Fourier Transform on the output representing the modulateddetected affected radiation and removing the dark current component fromthe transformed.

Preferably the processor identifies or verifies the drug sample usingreference information. Preferably the affected electromagnetic radiationat or the electromagnetic radiation beam comprising the anchorwavelength provides the reference information.

Preferably the analyser further comprises: an optical device fordirecting the plurality of electromagnetic radiation beams to areference sample, a reference detector that detects affectedelectromagnetic radiation beams affected by the reference sample toobtain the reference information and that passes the referenceinformation to the processor.

Preferably the detector and/or source are temperature compensated toprovide temperature stability, preferably using thermistors and peltierdevices in a closed loop system.

Preferably each electromagnetic radiation beam is a high intensitynarrowband light beam.

Preferably the detector is a broadband photodiode that is biased to havea response corresponding to the wavelength/s of the affected radiation.

Preferably the emitted electromagnetic radiation beams from theplurality of lasers are directed to a sample path by one or more of: acarousel or carriage device to position the laser beams in the samplepath, or a prism, diffraction grating, beam splitter or other opticaldevice to redirect a radiation beam along the sample path.

Preferably the processor receives: output representing the affectedelectromagnetic radiation from the drug sample which provides drugsample information, and optionally reference information for eachwavelength, and the processor: determines a representative value of thedrug sample information using that information and optionally referenceinformation for each wavelength.

Preferably the sample information and reference information correlateintensity and wavelength for each electromagnetic radiation beam.

Preferably the representative value corresponds to a best fit betweenthe sample information and optionally the reference information.

Preferably the representative value for the electromagnetic radiationbeam for each wavelength is compared to stored values to verify oridentify the drug sample.

Preferably the liquid is water, there are six electromagnetic radiationbeams and the wavelengths are substantially 1350 nm, 1450 nm, 1550, nm,1650, nm, 1750 nm and 1850 nm, and optionally wherein 1450 nm is theanchor wavelength.

Preferably the sample is in an intravenous delivery device such as an IVinfusions set or syringe, or other receptacle such as a test-cell,test-tube, flow cell or the like.

Preferably the source is a laser comprising a photodetector, wherein thephotodetector detects electromagnetic radiation from the laser andoutputs the reference information.

In another aspect the present invention may be said to consist in amethod for identifying or verifying or otherwise characterising a liquidbased drug sample comprising: emitting electromagnetic radiation in atleast one beam at a sample, the electromagnetic radiation comprising atleast two different wavelengths, detecting affected electromagneticradiation resulting from the emitted electromagnetic radiation affectedby the sample and providing output representing the detected affectedradiation, and identifying or verifying the sample from the outputrepresenting detected affected electromagnetic radiation, wherein eachwavelength or at least two of the wavelengths is between substantially1300 nm and 2000 nm, and each wavelength or at least two of thewavelengths is in the vicinity of the wavelength(s) of (or within aregion spanning) a spectral characteristic in the liquid spectrumbetween substantially 1300 nm and 2000 nm.

Preferably the electromagnetic radiation comprises a plurality ofelectromagnetic radiation beams, each beam having a differentwavelength.

Preferably verifying or identifying the drug sample is againstcomparison data for one of a set of n drugs, and wherein theelectromagnetic radiation comprises at least log₂n different wavelengthsin one or more beams.

Preferably the different wavelengths span or capture a plurality of atleast some of the spectral characteristics in the liquid spectrumbetween 1300 nm and 2000 nm.

Preferably the liquid spectrum comprises two or more spectralcharacteristics, and wherein: each spectral characteristic falls in orspans a region of the liquid spectrum, each wavelength falls within oneof the regions.

Preferably each region is defined by a wavelength range.

Preferably the spectral characteristics comprise peaks, troughs,inflections, stable points or regions, plateaus, knees and/or slopes ofthe liquid spectrum.

Preferably the liquid is water and comprises spectral characteristicsfalling in the following regions of the water spectrum: a first regionbetween 1300 nm and 1400 nm, a second region between 1400 nm and 1500nm, a third region between 1500 nm and 1600 nm, a fourth region between1600 nm and 1700 nm, a fifth region between 1700 nm and 1800 nm, and asixth region between 1800 nm and 200 nm.

Preferably the electromagnetic radiation has an anchor wavelength in thevicinity of the wavelength(s) of (or within a region spanning) a stableregion in the liquid spectrum.

Preferably each wavelength further corresponds to a wavelength producedby a source that is readily/cheaply obtainable.

Preferably the electromagnetic radiation is generated using a sourcecomprising a plurality of lasers, each laser configured to emit anelectromagnetic radiation beam at a fixed or tuneable wavelength.

Preferably wherein a modulator is used for modulating theelectromagnetic radiation beams emitted at the sample resulting indetected affected radiation that is modulated, and wherein identifyingor verifying the sample from the output from the output comprisesremoving the dark current component from the output representing thedetected affected modulated electromagnetic radiation.

Optionally removing the dark current component comprises multiplying theoutput representing the detected affected modulated electromagneticradiation by sine and cosine functions and integrating over the periodof modulation oscillation to remove the dark current component.

Optionally removing the dark current component comprises conducting aFourier Transform on the output representing the modulated detectedaffected radiation and removing the dark current component from thetransformed.

Preferably the identifying or verifying is carried out by a processorthat identifies or verifies the drug sample using reference information.

Preferably the affected electromagnetic radiation at or theelectromagnetic radiation beam comprising the anchor wavelength providesthe reference information.

Preferably the method further comprises: directing the plurality ofelectromagnetic radiation beams to a reference sample using an opticaldevice, detecting using a reference detector affected electromagneticradiation beams affected by the reference sample to obtain the referenceinformation and that passes the reference information to the processor.

Preferably the method further comprises temperature compensating thedetector and/or source provide temperature stability, preferably usingthermistors and peltier devices in a closed loop system.

Preferably each electromagnetic radiation beam is a high intensitynarrowband light beam.

Preferably the detector is a broadband photodiode that is biased to havea response corresponding to the wavelength/s of the affected radiation.

Preferably the emitted electromagnetic radiation beams from theplurality of lasers are directed to a sample path by one or more of: acarousel or carriage device to position the laser beams in the samplepath, or a prism, diffraction grating, beam splitter or other opticaldevice to redirect a radiation beam along the sample path.

Preferably the processor receives: affected electromagnetic radiationfrom the drug sample which provides drug sample information, andoptionally reference information for each wavelength, and the processor:determines a representative value of the drug sample information andoptionally reference information for each wavelength.

Preferably the sample information and reference information correlateintensity and wavelength for each electromagnetic radiation beam.

Preferably the representative value corresponds to a best fit betweenthe sample information and optionally the reference information.

Preferably the representative value for the electromagnetic radiationbeam for each wavelength is compared to stored values to verify oridentify the drug sample.

Preferably the liquid is water, there are six electromagnetic radiationbeams and the wavelengths are substantially 1350 nm, 1450 nm, 1550, nm,1650, nm, 1750 nm and 1850 nm, wherein 1450 nm is the anchor wavelength.

Preferably the sample is in an intravenous delivery device such as an IVinfusions set or syringe, or other receptacle such as a test-cell,test-tube, flow cell or the like.

Preferably each laser comprises a photodetector, wherein thephotodetector detects electromagnetic radiation from the laser andoutputs the reference information.

In another aspect the present invention may be said to consist in ananalyser for identifying or verifying or otherwise characterising a drugsample (or other substance) in a liquid carrier comprising: anelectromagnetic radiation source for emitting electromagnetic radiationin at least one beam at a sample, the electromagnetic radiationcomprising at least two different selected wavelengths, a sampledetector that detects affected electromagnetic radiation resulting fromthe emitted electromagnetic radiation affected by the sample, and aprocessor for identifying or verifying the sample from the detectedaffected electromagnetic radiation, wherein each wavelength is selectedto be in the vicinity of the wavelength(s) of (or within a regionspanning) a spectral characteristic in the spectrum of the liquidcarrier, each wavelength falling within an analysis range suitable forthe liquid carrier.

In another aspect the present invention may be said to consist in amethod for identifying or verifying or otherwise characterising a drugsample (or other substance) in a liquid carrier comprising: emittingelectromagnetic radiation in at least one beam at a sample, theelectromagnetic radiation comprising at least two different selectedwavelengths, detecting affected electromagnetic radiation resulting fromthe emitted electromagnetic radiation affected by the sample, andidentifying or verifying the sample from the detected affectedelectromagnetic radiation, wherein each wavelength is selected to be inthe vicinity of the wavelength(s) of (or within a region spanning) aspectral characteristic in the spectrum of the liquid carrier, eachwavelength falling within an analysis range suitable for the liquidcarrier.

In another aspect the present invention may be said to consist in ananalyser for identifying or verifying or otherwise characterising aliquid based drug sample (or other substance) comprising:

an electromagnetic radiation source for emitting electromagneticradiation in at least one beam at a sample, the electromagneticradiation comprising at least two different wavelengths, a sampledetector that detects affected electromagnetic radiation resulting fromthe emitted electromagnetic radiation affected by the sample, and aprocessor for identifying or verifying the sample from the detectedaffected electromagnetic radiation, wherein each wavelength is falls inan analysis range that provides improved identification/verification fordrugs in the liquid carrier, and each wavelength is in the vicinity ofthe wavelength(s) of (or within a region spanning) a spectralcharacteristic in the liquid spectrum in the analysis range.

In another aspect the present invention may be said to consist in amethod for identifying or verifying or otherwise characterising a liquidbased drug sample (or other substance) comprising: emittingelectromagnetic radiation in at least one beam at a sample, theelectromagnetic radiation comprising at least two different wavelengths,detecting affected electromagnetic radiation resulting from the emittedelectromagnetic radiation affected by the sample, and identifying orverifying the sample from the detected affected electromagneticradiation, wherein each wavelength is falls in an analysis range thatprovides improved identification/verification for drugs in the liquidcarrier, and each wavelength is in the vicinity of the wavelength(s) of(or within a region spanning) a spectral characteristic in the liquidspectrum in the analysis range.

In another aspect the present invention an analyser for identifying orverifying or otherwise characterising a liquid based drug samplecomprising: an electromagnetic radiation source for emitting modulatedelectromagnetic radiation in at least one beam at a sample, theelectromagnetic radiation comprising at least two different wavelengths,a sample detector that detects affected modulated electromagneticradiation resulting from the emitted electromagnetic radiation affectedby the sample and provides output representing the detected affectedmodulated radiation, and a processor for identifying or verifying thesample from the output representing detected affected modulatedelectromagnetic radiation including removing dark current from theoutput,

wherein each wavelength or at least two of the wavelengths is betweensubstantially 1300 nm and 2000 nm.

In another aspect the present invention a method for identifying orverifying or otherwise characterising a liquid based drug samplecomprising: emitting modulated electromagnetic radiation in at least onebeam at a sample, the electromagnetic radiation comprising at least twodifferent wavelengths, detecting affected modulated electromagneticradiation resulting from the emitted electromagnetic radiation affectedby the sample and providing output representing the detected affectedradiation, and identifying or verifying the sample from the outputrepresenting detected affected modulated electromagnetic radiationincluding removing dart current from the output, wherein each wavelengthor at least two of the wavelengths is between substantially 1300 nm and2000 nm.

In another aspect the present invention an analyser for identifying orverifying or otherwise characterising a liquid based drug samplecomprising: an electromagnetic radiation source for emittingelectromagnetic radiation in at least one beam at a sample, theelectromagnetic radiation comprising at least two different wavelengthsand for measuring the power of the emitted electromagnetic radiation, asample detector that detects affected electromagnetic radiationresulting from the emitted electromagnetic radiation affected by thesample and provides output representing the detected affected radiation,and a processor for identifying or verifying the sample from thedetector output representing the detected affected electromagneticradiation including using the measured power of the emittedelectromagnetic radiation, wherein each wavelength or at least two ofthe wavelengths is between substantially 1300 nm and 2000 nm, and eachwavelength or at least two of the wavelengths is in the vicinity of thewavelength(s) of (or within a region spanning) a spectral characteristicin the liquid spectrum between substantially 1300 nm and 2000 nm.

In another aspect the present invention a method for identifying orverifying or otherwise characterising a liquid based drug samplecomprising: emitting electromagnetic radiation in at least one beam at asample, the electromagnetic radiation comprising at least two differentwavelengths and measuring the power of the emitted electromagneticradiation, detecting affected electromagnetic radiation resulting fromthe emitted electromagnetic radiation affected by the sample andproviding output representing the detected affected radiation, andidentifying or verifying the sample from the output representingdetected affected electromagnetic radiation including using the measuredpower of the emitted electromagnetic radiation, wherein each wavelengthor at least two of the wavelengths is between substantially 1300 nm and2000 nm.

In another aspect the present invention a analyser for identifying orverifying or otherwise characterising a sample comprising: anelectromagnetic radiation source for emitting electromagnetic radiationin at least one beam at a sample, the electromagnetic radiationcomprising at least two different wavelengths, a sample detector thatdetects affected electromagnetic radiation resulting from the emittedelectromagnetic radiation affected by the sample, and a processor foridentifying or verifying the sample from the detected affectedelectromagnetic radiation, wherein each wavelength or at least two ofthe wavelengths is between substantially 1300 nm and 2000 nm.

Preferably the source is a plurality of lasers in a single package, eachlaser configured to emit an electromagnetic radiation beam at a fixed ortuneable wavelength.

It is intended that reference to a range of numbers disclosed herein(for example, 1 to 10) also incorporates reference to all rationalnumbers within that range (for example, 1, 1.1, 2, 3, 3.9, 4, 5, 6, 6.5,7, 8, 9 and 10) and also any range of rational numbers within that range(for example, 2 to 8, 1.5 to 5.5 and 3.1 to 4.7).

The term “comprising” as used in this specification means “consisting atleast in part of”. Related terms such as “comprise” and “comprised” areto be interpreted in the same manner.

This invention may also be said broadly to consist in the parts,elements and features referred to or indicated in the specification ofthe application, individually or collectively, and any or allcombinations of any two or more of said parts, elements or features, andwhere specific integers are mentioned herein which have knownequivalents in the art to which this invention relates, such knownequivalents are deemed to be incorporated herein as if individually setforth.

BRIEF DESCRIPTION OF THE DRAWINGS

Preferred embodiments of the invention will be described with referenceto the following drawings, of which:

FIG. 1 shows in schematic form a spectroscopic analyser according to thepresent invention,

FIG. 2 shows in schematic form the hypothetical spectrum of ahypothetical liquid base/carrier,

FIG. 3 is a graph showing the error vs. number of wavelengths used inthe spectroscopic analyser,

FIG. 4 is a flow diagram showing operation of the spectroscopicanalyser,

FIG. 5 shows the spectrum of a drug (gelofusine succinated gelatinesolution 4%) overlaid the spectrum of a liquid based, being water,

FIG. 6 shows spectral characteristics of water between 1300 and 2000 nm,

FIG. 7 shows a schematic diagram of a second embodiment of thespectroscopic analyser in which the sources are lasers on a rotatingcarousel,

FIG. 8 shows a method of processing the output from the detectors,including a pre-processing and a verification/identification stage,

FIG. 9 shows a method of processing the output from the detectors,including a pre-processing and comparison data generation stage,

FIG. 10 shows a best fit line through data points obtained from outputsfrom the sample and reference detectors,

FIG. 11 shows a separation line between pre-processed data points for atraining sample and a comparison sample,

FIG. 12 shows a third embodiment in which the source comprises sixlasers that are directed along the sample path 14 a using a diffractiongrating,

FIG. 13 shows a fourth embodiment comprising a source of six lasers theoutputs of which are directed along a sample path using beam splitters,

FIG. 14 shows in schematic form a fifth embodiment for the sourcecomprising six lasers the outputs of which are converged onto a samplepath using a prism,

FIG. 15 shows a matrix indicating verification for a set of sampledrugs,

FIG. 16 shows an analyser using source modulation to eliminate areference channel,

FIG. 17 shows laser output power where the source is modulated,

FIG. 18 shows a schematic diagram of an analyser with a modulator,

FIG. 19 shows a flow diagram for extracting dark current

FIG. 20 shows in schematic form a sixth embodiment for the sourcecomprising six lasers the outputs of which are converged onto a samplepath using a planar lightwave circuit,

FIG. 21 shows in schematic form a seventh embodiment for the sourcecomprising a single package source and collimated lens.

FIG. 22 shows a schematic diagram of a first embodiment of thespectroscopic analyser in which the sources are lasers in a singlepackage,

FIG. 23 is a flow diagram showing operation of the spectroscopicanalyser according to the first embodiment,

FIGS. 24 and 25 are flow diagrams showing theverification/identification process in more detail.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

Overview

FIG. 1 shows an overview of a spectroscopic analyser 10 (for example, aspectrophotometer) according to the present invention for verifying oridentifying (that is, analyse/characterise) drugs or other samples (e.g.blood, biological samples, etc.). The term “drug” should be interpretedbroadly to cover any pharmaceutical or other medicament or substance fortreating patients, which is clinician controlled 9 (e.g. through ahospital, prescription or pharmacy) or freely available. The analysercan be used for blind tests, wherein an unknown sample is analysed to beverified/identified or otherwise characterised. The analyser can also beused to obtain training data from test samples during a training processto assist in the later analysis of an unknown sample in a blind test.

The analyser (apparatus) 10 comprises a controller 12 that controls bothphysical control and processing aspects of operation. The analyser 10comprises an electromagnetic radiation source 11 for generating andemitting electromagnetic radiation 22 with/at a plurality of wavelengthswithin a wavelength range. The source might also have a photodetector 4or similar for control purposes. The electromagnetic radiation couldtake the form of a plurality of electromagnetic radiation beams atdifferent wavelengths, or a single electromagnetic radiation beamcomprising a plurality of wavelength components.

The term “wavelength” used for electromagnetic radiation output refersto a particular wavelength, such as 1300 nm. As will be appreciated, inpractice, a source will not provide electromagnetic radiation outputwith a pure single wavelength—the output could contain components eitherside of the centre wavelength/peak. In this case, the term “wavelength”refers to the centre wavelength/peak of the electromagnetic radiationoutput, where the radiation output might also have a wavelengthcomponents either side of the centre wavelength, e.g. +/−30 nm, or +/−12nm or even just a few nm (e.g. 2 nm for lasers) either side. Each suchwavelength could be termed a “discrete” wavelength, as for practicalpurposes it is discrete, even if other components exist.

The electromagnetic radiation beams 22 could be visible light beamsemitted from one or more lasers, for example. In one example, theelectromagnetic radiation source (“source”) 11 could be a single devicethat can be configured to generate and emit a plurality ofelectromagnetic radiation beams with different wavelengths in sequenceor simultaneously, or that emits a single electromagnetic radiation beamwith multiple wavelength components. In another example, the source 11could be a set of individual sources, each configured to generate andemit electromagnetic radiation beams 22 with a desired wavelength. Theterm “source” can refer to a single source or multiple sources making upa source. In each case, the source 11 might generate a fixed wavelengthelectromagnetic radiation beam(s), or it might be tuneable to emit anelectromagnetic radiation beam(s) at one of a range of wavelengths. Thesource electromagnetic radiation might optionally be modulated asdescribed later. Other examples could be envisaged by those skilled inthe art also. The source can have an inbuilt or separate temperaturesensor 2 a (which may form part of the photodiode 4), such as athermistor for detecting the operating temperature. The output can bepassed to the processor 18.

Preferably, the source 11 is configured so that each electromagneticradiation beam 22 with a corresponding wavelength(s) can beindependently emitted in sequence. This might be achieved through usinga single source that is tuned to emit electromagnetic radiation beamsthat sweep through a range of wavelengths. Alternatively, where a sourcecomprises multiple electromagnetic radiation sources, each of which canbe operated in turn, it might be achieved by each source becoming the“active” source—such as a single package comprising multiple lasers. Sothat the electromagnetic radiation beam of the active source is directedalong the desired sample path 14 a, each electromagnetic radiation beamoutput from the source can be arranged to hit a grating, mirror, prismor other optical apparatus 13 that redirects the beam from that sourcealong the desired sample path 14 a. In such arrangement, eachelectromagnetic radiation beam can be directed in sequence along thedesired path as it is generated/activated. Alternatively, multipleelectromagnetic radiation beams could be simultaneously directed along abeam path 14 a, resulting in a single beam of electromagnetic radiationcomprising a plurality of wavelength components. Alternatively, thesources could be arranged on a carousel or linear carriage (alsorepresented by 13) that can be mechanically controlled to physicallyposition each source to emit a radiation beam along the path 14 a. Thesealternatives will be described further later. Other arrangements forredirecting a plurality of electromagnetic radiation beams from a source11 along a desired path 14 a could also be envisaged. Theelectromagnetic radiation beam directed along the path 14 a can betermed the sample electromagnetic radiation beam.

The apparatus 10 comprises a sample/sample retainer 16 for holding asample in the path 14 a of the sample electromagnetic radiation beam. Anon-contact infrared or other temperature sensor 71 is incorporated intoor disposed near the sample retainer 16 to enable a measurement to bemade of the temperature of the sample under test and the retainer. Thiscould be the same or separate to the retainer temperature sensor 2 b, 2c.

The sample retainer 16 could be a test-tube/test-tube holder, other typeof test cell, part of an infusion pump/IV set, flow-cell, syringe or anyother type of device for holding any of these or for holding asample/substance in any manner. The sample could alternatively simply beplaced in the path 14 a. Any sample retainer allows for transmission ofthe electromagnetic radiation 22 to and through the sample. The sampleis preferably (although not limited to) a liquid based drug. The liquidbased sample could, for example be a water based drug, but it could alsobe another type of sample/substance in water or other liquid carrier.The use of “drug” in the embodiments below is for illustrative purposesonly and it will be appreciated that the embodiments could be used forother types of samples. The term “sample” is used generally to indicatea substance for analysis (e.g. verification/identification) and is notnecessarily restricted to a test sample/small portion of a larger amountof substance. For example, the sample could be an actual drug to beadministered—not simply a (sample) portion of that drug to beadministered. The apparatus 10 can be used in a clinical or otherenvironment to verify/identify a drug prior to admission. In this case,the sample put in the apparatus 10 will be the actual drug beingadministered. The sample can be a training sample, or an unknown sampleunder test. The retainer in the sample and reference channels can havean inbuilt or separate temperature sensor, such as a thermistor, fordetecting the retainer temperature, 2 a, 2 c and/or the sample. Theoutput can be passed to the processor 18.

An electromagnetic radiation beam emitted along the path 14 a providesincident electromagnetic radiation on a sample (substance) 16 placed inthe path (e.g. in the sample retainer.) Any incident electromagneticradiation beam 14 a that reaches the sample 16 is affected by the sample(e.g. either by transmission through and/or reflection by the sample.)The affected (sample) electromagnetic radiation 14 b that exits thesample 16 is affected electromagnetic radiation and contains spectralinformation regarding the sample. Spectral information broadly means anyinformation contained in affected electromagnetic radiation. Forexample, the affected electromagnetic radiation 14 b comprisesinformation about the intensity of the affected electromagneticradiation at one wavelength of the incident radiation.

A sample detector 17 is placed in the affected electromagnetic radiationpath 14 b such that affected electromagnetic radiation 14 b exiting thesample can be detected. The detector 17 can comprise, for example, oneor more photodetectors. The detector 17 outputs information 14 c in theform of data/a signal that represents or indicates spectral informationof the sample 16—that is, the output represents the detected affectedelectromagnetic radiation. The detector output 14 c could, for examplerepresent or provide an indication of the electromagnetic intensity ofthe affected electromagnetic radiation incident on thedetector—typically in the form of a voltage that is proportional to theintensity. It will be appreciated that while the output might notactually be the electromagnetic radiation intensity, it will have somerelationship to it, such as being a signal with a voltage beingproportional to the actual intensity. Use of the term “intensity”throughout this specification when referring to the detector output willbe understood to not be limiting and could relate to any parameterrelating to intensity. The detector 17 output 14 c is passed through toa processor 18 that carries out a verification/identification algorithmin order to verify or identify or otherwise analyse the sample in theretainer. Pre-processing can optionally occur, although this is notessential; for example if a stable source is used such as a laser. Theprocessor 18 can form part of the controller 12, or can be separatethereto. The processor 18 comprises or has access to a database 23 withreference/training/comparison data for verifying or identifying orotherwise analysing the sample. The database 23 is a datastore and cantake any suitable form and use any suitable hardware (such as memory inthe processor or an external or even remote hardware). It is notnecessarily part of the processor 18, but is shown as such forsimplicity. The path 14 a, 14 b, emitted and affected radiation and/orthe sample/sample holder 16 can be termed the “sample channel.” Thesample detector 16 and inputs to the processor 18 (and optionally theprocessor itself) can also form part of the sample channel.

Optionally there might also be a reference channel, in which the emittedelectromagnetic radiation beam 14 a incident on the sample 16 is split21 or otherwise redirected along a reference path 15 a towards anotherretainer 19 containing a reference sample/substance (or simply“reference”) 19. A beam splitter 21 could be used to achieve this. Thereference could be saline, for example. The reference sample retainer 19could be any one of those retainers 16 mentioned with respect to thesample channel. Alternatively, the reference may have no retainer and/orsample and be for the purposes of measuring uninterruptedelectromagnetic radiation. The reference channel, while shown as aseparate channel, could in fact be the same as the sample channel, butreconfigured to remove the sample and/or retainer and place theappropriate reference sample (if any) in the electromagnetic radiationpath. The reference electromagnetic radiation beam along the referencepath 15 a is incident on and affected by the reference sample 19 (ifany) to produce affected (reference) electromagnetic radiation 15 bwhich is incident on and detected by a reference detector 20. Thereference detector 20 could be the same or different detector to that ofthe sample channel. In FIG. 1, the reference detector 20 is shown as anindependent detector by way of example.

The reference detector 20 outputs information 15 c in the form of data/asignal that represents or indicates spectral information 15 c of thereference—that is, the output represents the detected affectedelectromagnetic radiation. The detector output 15 c could, for example,represent the electromagnetic intensity of the affected electromagneticradiation such as described earlier for the sample channel. The detectoroutput 15 c is passed through to the processor 18 that carries out averification/identification algorithm in order to verify or identify thesample 16 in the retainer. Pre-processing can be carried out, althoughthis is not essential if a stable source is used, such as a laser. Thedetector output 15 c from the reference channel provides data from whichto normalise and/or correct the sample channel data 14 c. The referencechannel might also comprise a neutral density filter prior to thesample. This attenuates the incident electromagnetic radiation in amanner to normalise the detected affected electromagnetic radiation, orotherwise modify it so that the output of the detector is at a suitablelevel to enable processing/comparison with the output of the detector onthe sample channel.

In an alternative to the reference channel, optionally the output from amonitor diode 4 on the source could be used to provide referencedata/output/signal/information from which to normalise and/or correctthe sample channel data 14 c. The output can be provided to thecontroller 12 and/or processor 18. The monitor diode could be apre-existing detector on the source that measures power of the outputelectromagnetic radiation. In this case, the monitor diode could beconsidered a “reference detector” and in effect provide a referencechannel.

Each electromagnetic radiation beam 22 has a wavelength (or has aplurality of wavelength components) that falls in the analysis range(“analysis region”), preferably of 1300-2000 nanometers (nm). Thisregion can nominally be termed “near infrared” or “NIR”. This regionprovides useful spectral information for verifying or identifying drugs.The wavelength of each electromagnetic radiation beam 22 (or thewavelengths making up an electromagnetic beam) is preferably selectedbased on spectral characteristics (features) of the base liquid of thedrug sample that fall within the analysis range. Such characteristicscould be, for example, peaks, troughs, points of inflection, stablepoint or regions, plateaus, knees and/or slopes of that base liquidspectrum. Each wavelength selected is in the vicinity of (or within aregion spanning) such a spectral characteristic. The position of aspectral characteristic could be defined by a nominal wavelength (of forexample the centre wavelength of the characteristic) or a range ofwavelengths defining a region spanning the characteristic.

Selection of each wavelength can be demonstrated with reference to thespectrum of a hypothetical base liquid as shown in FIG. 2. Thehypothetical spectrum comprises the following spectral characteristicsA-E in the analysis range.

-   -   A peak between 1300 nm and 1400 nm (centre wavelength of 1350 nm        of actual peak) (A).    -   A trough between 1400 nm and 1500 nm (centre wavelength of 1450        nm of actual trough) (B).    -   An inflection between 1500 nm and 1600 nm (centre wavelength of        1550 of actual inflection) (C).    -   A slope between 1600 nm and 1800 nm (D).    -   A plateau between 1800 nm and 2000 nm (E).    -   A knee is also shown around 1800 nm between characteristics D        and E.

For analysis of drugs with this hypothetical liquid as a base,wavelengths could be chosen that are within the vicinity of thewavelength ranges (or centre wavelength) for one or more of the spectralfeatures A-E above, or that fall within in a region spanning(defining/delimiting) the wavelength ranges for one or more of thespectral features A-E above. A wavelength in the “vicinity” of aspectral characteristic also can mean a wavelength at the spectralcharacteristic centre wavelength. For example, three differentwavelengths could be chosen as follows.

-   -   Wavelength #1 1310 nm—within the region 1300-1400 nm for feature        A.    -   Wavelength #2 1450 nm, roughly at or within the vicinity of the        centre wavelength of feature B.    -   Wavelength #3 1800 nm, at the edge/knee (i.e. within the region)        of feature E.

The chosen discrete wavelengths that relate to spectral characteristicsof the liquid spectrum can be termed “selected wavelengths” or “chosenwavelengths”. In general terms, the selected or chosen wavelengths“correspond” to or “capture” a spectral characteristic.

It will be appreciated that FIG. 2 shows just some hypothetical examplesof spectral characteristics (features)—many more are possible for aspectrum. Further, the wavelength ranges for spectral characteristicscould overlap or even coincide. Further, a separate wavelength need notbe chosen for each spectral characteristic in the analysis range—just aselection of wavelengths relating to a selection of spectralcharacteristics might be chosen. It might not be possible to define aspectral characteristic by a wavelength range, or any such range mightvary depending on interpretation. A wavelength in the vicinity of aspectral characteristic might instead be chosen. This could be awavelength that is near or within a certain tolerance (e.g. +/−30 nm) ofthe centre point wavelength of a spectral characteristic, for example.

In addition, the selected wavelength might be influenced by sources 11that are readily obtainable or configurable to a wavelength that is inthe vicinity of or falls within in a region spanning such a spectralcharacteristic. The selection of suitable wavelengths for the emittedradiation will provide better information for accurate verification oridentification by the processor.

In addition, preferably, the selected wavelengths can be selectedindependently from the drug(s) being tested.

It will be appreciated that the wavelengths could be selected in anyother suitable manner, such as by randomly or evenly spacing them acrossthe region, or using some other selection criteria.

Any suitable number of wavelengths can be used. Optionally, although notessentially, the number of different wavelengths constituting theelectromagnetic radiation (either in one or multiple beams 22) providedby the source 11 is at least log₂n, where n is the number of samplesthat are tested for. The more wavelengths that are used, the better theaccuracy, but this is optimised against costs and convenience. As seenin FIG. 3, as the number of electromagnetic radiation beams/wavelengthsincreases, the error of detection decreases. A selection of twowavelengths provides an error of 0.14 for a set of 30 drugs, whereasfive wavelengths provide an error of just 0.02.

One of the electromagnetic radiation wavelengths 22 can optionally beselected to have a wavelength at an anchor point, which can be used toeliminate the need for a reference channel. The anchor point is chosento have a wavelength in a stable or other suitable portion of thespectrum of the underlying base liquid. The anchor wavelength isdescribed further later.

Upon receiving output from a sample detector 17 and optionally areference detector 20 (or alternatively output from a monitor diode thatmeasures power of output electromagnetic radiation), the processor 18executes an algorithm that accesses a database 23 comprisingtraining/comparison data (possibly in the form of a look up table), anduses that output to verify or identify (“characterise”) the sample 16based on the affected electromagnetic radiation 14 b detected from thesample 16, and optionally:

a) where a reference channel is used, the output of the detectedaffected radiation 15 b from that reference sample, or

b) where a source monitor diode is used, measured power of the outputsource electromagnetic radiation,

using the training/comparison data. The training/comparison data can beobtained in a previous analysis of training samples using the analyser.In one option, the raw training/comparison data obtained from thedetector(s) is processed to obtain training/comparison coefficients thatcan be used for characterisation of an actual unknown sample in a blindtest.

The processor 18 can operate with or independently from the controller12. Processing will be described further later.

In addition to or as part of the verification/identification process oneor more of the following can be undertaken.

-   -   Measurement of the sample (including where appropriate the        retainer) temperature and correction of the training/comparison        data based on the sample temperature.    -   Determining parameters (coefficients) representative of the        sample or training data/sample that are independent of sample        concentration that can be referenced against parameters        representative of the comparison data/comparison sample for        identification/verification.    -   Determining concentration of the sample.    -   Processing raw training/comparison data and actual sample data        to reduce inaccuracies caused by dimension tolerances in the        system including the sample retainer (e.g. a test-tube/test-tube        holder, other type of test cell, part of an infusion pump/IV        set, flow-cell, syringe or any other type of device for holding        any of these or holding a sample/substance in any manner.)    -   Determine and/or eliminate the dark current of the        photodetectors using either a technique involving a modulated        source or dark current measured using a chopper wheel        arrangement.

A user interface 24 allows a user to operate the apparatus 10, includingsetting parameters, inputting anticipated drugs (e.g. for verification)or other sample identification and receiving the results of analysis(via a screen, display, audio alarm, indicator or similar). The resultsmight indicate whether the drug is as anticipated(verification/confirmation), or might advise of the drug(identification) and/or might indicate concentration of the sample underblind test.

The controller 12 and/or processor 18 might also control an externaldevice (such as an infusion pump) to allow or prevent delivery of a drugbased on the test result.

Preferably, the apparatus 10 also comprises a feedback system tostabilise the temperature of the electromagnetic radiation source 11and/or the detectors(s) 17, 20. In one example, thermistors detect thetemperature of the electromagnetic radiation source and/or detector(s)and/or also optionally the sample retainer 2 a, 2 b, 2 c, 5 a, 5 b.Peltier cooling devices can be operated to cool and stabilise thetemperate of the source 11 and detectors 17, 20. The output of thethermistor(s) is sent to the controller 12, which controls the peltiercooling devices to cool the source and/or detectors. Preferably thethermistor is the built-in photodetector/source thermistor 2 a, 2 b, 2c, 5 a, 5 b, and the peltier thermo-electric cooler is built-in to thephotodetector/source 2 a, 2 b, 2 c, 5 a, 5 b.

The apparatus 10 works generally as follows, with reference to the flowdiagram in FIG. 4. The controller 12 is used to operate the source 11 toemit one or more electromagnetic radiation beams 22 (preferably—althoughnot essentially— individually and in sequence) with/at the selectedwavelengths towards the sample 16, step 40. The electromagneticradiation incident 14 a on a sample 16 is transmitted or reflectedthrough the sample and becomes affected electromagnetic radiation 14 bwhich is detected by the detector 17, step 41. Optionally, the emittedradiation may be diverted by a beam splitter 21 also to a referencesample 19 (of free-space path), which is detected by the same or adifferent detector 20, step 42. The outputs 14 c, 15 c from the sampledetector 17 and optionally the reference detector 20 are passed to theprocessor 18, step 42. Here pre-processing takes place to normaliseand/or correct the detector output 14 c, 15 c, step 42 if required. Thenthe identification/verification algorithm is executed, step 43, whichincludes querying the database 23 of reference drugs, the informationfrom which (e.g. training/comparison data) being utilised to identify orverify the sample from the normalised detector output. The result of theverification or identification of the sample is communicated by the userinterface 24, step 44.

Other options will become apparent as a more detailed description of theinvention is provided.

First Embodiment

One embodiment of the invention will now be described in detail by wayof example. This should not be considered limiting but illustrative. Theembodiment is described in relation to an apparatus for providingverification or identification of water or other liquid based drugs frome.g. a set of 15 drugs set out in the table below. While in thisembodiment the sample is referred to as a drug, more generally theembodiment could be applied to any other sample type.

Six wavelengths of electromagnetic radiation are chosen for thisexample, six being greater than log₂n of 30. The wavelengths are chosenin the analysis range and are based on the spectral characteristics ofwater, being the base liquid, falling in that range. The spectrum of awater based drug (or other liquid based drug or aqueous solution) willbe heavily dominated by the base liquid spectrum. For example referringto FIG. 5, the spectrum (dotted line) of drug W (gelofusine succinatedgelatine solution 4%) is very similar to the spectrum of water (solidline). This is because the spectrum of water dominates. However, thedifferences in transmission coefficient between different water baseddrugs can be measured. Focussing on areas/wavelengths of spectralcharacteristics of the water spectrum, by using electromagneticradiation beams at those wavelengths, the difference between the waterspectrum and the water based drug spectrum at those wavelengths can beutilised to provide drug discrimination for drug identification orverification.

FIG. 6 shows a spectrum of water with some possible spectralcharacteristics (features) in the analysis range identified, andexplained further below.

-   -   Spectral characteristic A (slope)—in a first region between 1300        nm and 1400 nm.    -   Spectral characteristic B (plateau/trough)—in a second region        between 1400 nm and 1500 nm.    -   Spectral characteristic C (slope)—in a third region between 1500        nm and 1600 nm.    -   Spectral characteristic D (peak)—in a fourth region between 1600        nm and 1700 nm.    -   Spectral characteristic E (inflection)—in a fifth region between        1700 nm and 1800 nm.    -   Spectral characteristic F (knee) a sixth region between 1800 nm        and 2000 nm.

This is not an exhaustive list of possible spectral features.

The selection of a wavelength for an electromagnetic radiation beam isnot strictly fixed, and not necessarily solely based on spectralcharacteristics of the base liquid. It is influenced by the wavelengthof spectral characteristics in spectrum of the base water of the drugsample, but in addition the selected wavelength can be based on otherfactors also. For example, in interest of cost effectiveness and aregularly obtainable supply chain, it might be preferable to use orselect an alternative wavelength that is close to the spectralcharacteristic but not quite the same, if that alternative wavelength iseasily obtainable by an off-the-shelf laser or other optical component.

For example, it is possible to use 1310 and 1550 nm as selectedwavelengths for water based drugs as there are many devices configuredfor these wavelengths as they have wide spread use within thecommunications industry. Laser diodes nominally have centred wavelengthsat 1650 nanometers, 1750 nanometers and 1850 nanometers, although thesecan be varied by up to plus or minus 30 nanometers. So wavelengths inthese ranges can also be selected. Therefore by looking at theavailability of these components, and the spectral characteristics ofthe base liquid, suitable wavelengths for the emitted radiation can bedetermined.

Therefore, based on the above explanation, each of the six wavelengthscan be chosen to be within the vicinity or within the region spanningone of each of the spectral features, but also influenced by theavailability of hardware. The six wavelengths for water could thereforebe (by way of example): 1350 nanometers corresponding to feature A, 1450nanometers corresponding to feature B, 1550 nanometers corresponding tofeature C, 1650 nanometers corresponding to feature D, 1750 nanometerscorresponding to feature E and 1850 nanometers corresponding to featureF, all which fall within the 1300-2000 nanometers. As can be seen the1350 nm to 1850 nm wavelength selections do not match exactly to peaksand troughs and other spectral characteristics in the water spectrum,although are close. The selections also relate to operating wavelengthsof available hardware. These are of course nominal wavelengths and theactual wavelength might vary in practice due to source 11characteristics. It should also be noted that arbitrary wavelengthscould be chosen spread across the region, rather than selected atspecific spectral features.

FIG. 22 shows in schematic form one possible form of the apparatus 10 asgenerally described in FIG. 1. The spectroscopic analyser 10 has acontroller 12 and a single laser package (more generally “laser”) thatcontains six laser modules 51 a-51 f, which together form the source 11to output electromagnetic radiation 22 at a plurality of wavelengths inthe form of light. The single package 211 comprises 6 lasers forming thesource 11 that are arranged to emit their electromagnetic radiation beam22 (which could be any one of wavelengths 201 a-201 f) towards anintegrated collimating lens 210. The package is operable to emit a tunedor tuneable wavelength at each of six wavelengths 201 a-201 f towardsthe lens 210. The package comprises one or more laser diodes providing astable, high intensity, narrow band collimated electromagnetic radiationoutput that is controlled electronically via controller 12. Thecontroller can have a user interface 24 for user input and output. Thesource can have an inbuilt or separate temperature sensor 2 a, such as athermistor for detecting the operating temperature. The output can bepassed to the processor 18.

The controller 12 activates the laser package to sequentially orotherwise to emit a beam 201 a-201 f of a single wavelength towards thesample. Alternatively, multiple beams 201 a-201 f could be operated atonce such that an electromagnetic beam 22 comprising multiple wavelengthcomponents (e.g. 201 a-201 f or a subset thereof) could be emittedtowards 14 a the sample 16 via the lens 210.

The apparatus comprises a modulator 70, which can be a separate devicecoupled to the laser package 211 or incorporated into the controller 12,or it can be incorporated into the laser package itself. The modulator70 controls the laser package 211 to modulate the output electromagneticradiation 22. Modulating the electromagnetic radiation allows forprocessing to account for dark current as will be described below.

The package 211 comprises one or more monitor photodiodes 4 a fordetecting output electromagnetic radiation 22 (e.g. for measuring outputpower of the electromagnetic radiation) for feedback control of thatradiation. This can be combined with the temperature sensor 2 a. Theoutput is provided to the processor 18 either directly or via thecontroller 12. Lasers have fewer heat emission problems than othersources, thus reducing the detrimental effects of heat on themeasurements. The output power of each laser preferably is nominally thesame (typically 2-3 mW although could be more) in the interests ofhaving a balanced apparatus. Preferably, this also enables a commondiode driver circuit to be used for the laser diodes.

There is also a temperature sensor 71 (e.g. non-contact infrared sensor)for measuring the sample 16 under test and its retainer. There may be acombined or separate temperature sensor for measuring the retainertemperature as well. The outputs are provided to the processor 18,either directly or via the controller 12.

Once activated, the laser 211 emits (preferably modulated)electromagnetic radiation 22 towards the sample along the path 14 a viathe lens 210. The path 14 a from the source to the detector is acombination of free-space with optical fibre components. This reducesoptical attenuation and hardware. The apparatus also comprises a sampleretainer 16 a, which is aligned with the beam path 14 a. The emittedelectromagnetic radiation from an active laser 51 a-51 f is incident onand transmits or reflects through the sample 16 in the sample retainer.

The detector 17 is placed in the affected radiation path 14 b that exitsthe sample 16. Preferably the detector 17 is a single photodetector(such as a photodiode) biased to have a suitable response to detectelectromagnetic radiation of wavelengths that will be in the affectedradiation. A single detector reduces the errors due to variabilityintroduced by components—it removes the relative differences betweenmultiple photodetectors enabling a more stable response to the output ofthe emitted electromagnetic radiation thus enhancing sensitivity. AnInGaAs photodiode could be used, for example. The detector 17 detectsthe affected radiation 14 b and the output 14 c of the detector 17 ispassed to a processor 18 that using previously obtainedtraining/comparison data in a database 23 verifies or identifies orotherwise characterises the sample as described herein. In addition toor as part of that process the processor 18 also undertakes thefollowing.

-   -   Measurement of the sample (including where appropriate the        retainer) temperature and correction of the training/comparison        data based on the sample temperature.    -   Determining parameters (coefficients) representative of the        sample or training data/sample that are independent of sample        concentration that can be referenced against parameters        representative of the comparison data/comparison sample for        identification/verification.    -   Determining concentration of the sample.    -   Processing raw training/comparison data and actual sample data        to reduce inaccuracies caused by dimension tolerances in the        system including the sample retainer (e.g. a test-tube/test-tube        holder, other type of test cell, part of an infusion pump/IV        set, flow-cell, syringe or any other type of device for holding        any of these or holding a sample/substance in any manner.)    -   Determine and/or eliminate the dark current of the        photodetectors using either a technique involving a modulated        source or dark current measured using a chopper wheel        arrangement.

Preferably, the apparatus also comprises a feedback system to stabilisethe temperature of the electromagnetic radiation source 11 and thedetectors(s). In one example, thermistors 2 a, 71, 5 a detect thetemperature of the electromagnetic radiation source and/or detector(s)and/or retainer. Peltier cooling devices can be operated to cool andstabilise the temperate of the source and detectors. The output of thethermistor(s) is sent to the controller, which controls the Peltiercooling devices to cool the source and/or detectors. Preferably thethermistor is the built-in photodetector/source thermistor 2 a, 71, 5 a,and the peltier thermo-electric cooler is built-in to the photodetector2 a, 5 a.

The apparatus/analyser 10 is used to obtain raw training/comparison datafrom training samples carried out during a training process/test. Italso obtains raw data of an actual unknown sample under test during ablind test. It can process the raw training/comparison data and/or theraw data of the sample under test to obtain coefficients (comparisondata) that can be utilised in a process to characterise the unknownsample in the blind test.

Referring to FIG. 23 (which is based on but provides more detail thanFigure), operation of the apparatus 10 will now be described for a blindtest. A blind test is where an actual unknown sample for verification oridentification or other characterisation is tested. An unknown sample 16to be tested is placed in the retainer or otherwise placed in orintroduced to the analyser 10. The controller 12 operates the laser 211to emit an electromagnetic radiation beam 22 at one of the selectedwavelengths 201 a-201 f to the sample 16, step 230 As part of this,preferably the modulator 70/controller 12 operates the laser 211 tomodulate the electromagnetic source radiation beam 20, step 230, in amanner to be described below with respect to the processor 18. In thismanner, six modulated electromagnetic source radiation beams 201 a-201 fwith different selected wavelengths can be emitted, step 230, insequence from the laser 211, each tuned to a different selectedwavelength. The temperature of the sample is measured and recorded foreach test at any suitable time in the process, e.g. at the same time asemitting the radiation, step 230.

Each electromagnetic beam 22 is emitted via the integrated collimatinglens 210 along the path 14 a towards the sample 16. The affectedradiation coming from the sample is detected by the photodetector 17,step 231, for each electromagnetic radiation beam emitted 14 a towardsthe sample 16.

Optionally, the monitor diode 4 a in the laser 211 measures the power ofthe output electromagnetic radiation beam 22 to obtain referenceinformation. Alternative, reference information can be obtained using areference channel such as shown in FIG. 1 or FIG. 18.

The output (electromagnetic radiation intensity measurements) from thesample detector 17 and optionally the monitor diode 4 in the sourcelaser 211 are passed to the processor 18 and/or database 23 where it isstored as data, step 232, for identification/verification of the sample16 under test. The temperature measurement is also passed to theprocessor 18 and/or database 23. The output 14 c received at theprocessor 18 from the sample detector 17 or from the monitor diode 4indicates the intensity of the affected electromagnetic radiation 14 bfor each emitted electromagnetic radiation beam at the sample 16. Itmay, for example, comprise data which directly or indirectly indicatesphotocurrent of the detector (such as a voltage proportional tointensity) and/or intensity of the detected electromagnetic radiation.In this case of modulated source (as discussed below) a modulatedwaveform output is received which is digitised. The steps 230-232 arepreferably repeated several times for each wavelength to obtain multipleintensity measurements that can be processed to obtain an average orother representative intensity for each wavelength, step 233. Forexample, at each wavelength, the analyser detects affectedelectromagnetic radiation affected by the sample at 25 different timesand passes this output to the processor 18 and/or database 23, step230-233. Once the process has been completed for one wavelength, theprocess, steps 230-233, is repeated for the remaining wavelengths, step234. The temperature measurement can be taken during each iteration alsoand stored in the processor/database as appropriate, step 230. Theintensity and temperature data in the processor/database can be termed“blind test raw data”.

Once all the intensity, temperature and any other measurements have beenreceived by the processor 18, verification or identification can takeplace, step 235. Identification or verification of a sample is based ontraining data (also termed “comparison data”) that has previously beengenerated or otherwise obtained. In this embodiment, sample coefficientsor other data representing the sample under blind test areobtained/determined from the blind test raw data during theidentification/verification process and these are compared tocorresponding training coefficients or other comparison data obtainedfrom test samples during a training process. If the coefficients orother data of the sample under test match to the required similarity tothose of a test sample, then a verification or identification can bemade.

It will be appreciated that in general terms, the raw training dataand/or blind test raw data can be used as is or processed in anysuitable way to undertake characterisation of the unknown sample undertest. The coefficients described in this embodiment demonstrate one wayin which to use the raw data. “Training data” can refer to raw trainingdata in its unprocessed form, or processed raw training data.Furthermore, “comparison data” can refer to processed or unprocessed rawtraining data and/or processed or unprocessed raw blind test data.Comparison data refers to any data that can be used to characterise anunknown sample under blind test.

Verification involves confirming that a sample drug is the drug that isexpected. For example, a clinician can specify what they think the drugis (e.g. from the set of n drugs) through the user interface 24 step 85,then use the apparatus to confirm whether the drug in the retainer isactually that drug which is specified by the clinician. Identificationinvolves determining what a drug actually is, without any suggestionfrom the clinician as to what the drug is. Forverification/identification, the blind test raw data are processed andare compared against the processed raw training data in the database 23,step 85, to identify the drug, or verify whether it is the anticipateddrug as specified by the clinician. Output is then provided to the userinterface, step 87.

The verification/identification processing will now be described in moredetail. However, as the verification/identification processing utilisestraining data, the acquisition and (optional) processing of trainingdata will be described first with reference to FIG. 23.

Acquisition of Training Data

In overview, training data is obtained during a training process at somepoint prior to verification/identification of an actual unknown sampletaking place in a blind test. It can be obtained once, or periodicallyupdated. It is stored in the processor 18 and/or the database 23, eitherintegrated with or accessible by the processor 18 for use duringverification/identification. As mentioned above, the terms “trainingdata” and “comparision data” in general can refer to raw data obtainedduring a training process, or raw data that has subsequently beenpost-processed for utilisation in the identification/verificationprocess. The training data is obtained from known samples against whichdata from blind test samples will be analysed. Preferably, any unknownsample type (e.g. a particular drug) that may be tested for in a blindtest will have corresponding training data previously obtained from thesame sample type (e.g. drug). A set of training samples (e.g. a set ofdrugs) corresponding to those that may be tested for, are obtained,analysed in the training process and raw training data obtained for themand stored. The raw training data is obtained in the same way as actualthe blind test data is obtain as described herein, e.g. as shown in FIG.23 using e.g. the apparatus in FIG. 22 or any of the other embodimentsdescribed.

As an example, with reference to FIG. 23, a set of test (training)samples (e.g. different training drugs/dilutants such as those in thetable below) are obtained, step 237. The samples comprise a range ofundiluted drugs of known concentration and dilutants of interest (e.g.0.9% saline, 5% glucose) being the dilutants in which a drug may bediluted in for an actual blind test. Each one is analysed in turn, usinge.g. the analyser of FIG. 22. As described previously for the actualblind test, the training drug is placed in a retainer, and (optionallymodulated) electromagnetic radiation of different wavelengths is emittedat the drug in the retainer in sequence, step 238. The intensity of theaffected electromagnetic radiation from the drug at each wavelength isdetected by a detector, step 239 and is passed to the processor 18,and/or database 23, step 240. Preferably, each wavelength ofelectromagnetic radiation can be emitted multiple times, step 241, andthe detector intensity output/measurement from each is averaged orotherwise processed in the processor to obtain the raw training data.Once one wavelength is complete, the sample is tested at the nextwavelength 242. Each drug can also be tested multiple times at eachwavelength in a different retainer (e.g. different test tubes) toaverage out variations in each retainer, step 243. The temperature ateach measurement at each wavelength for the lasers, detectors andsample/retainer can also be taken and passed to the processor/databasefor storing along with the intensity measurement, steps 238, 240. Thisis repeated for each sample drug, step 244. Note, while the FIG. 23shows that each wavelength is tested multiple times, then the retaineris changed, alternative orders could occur—such as the retainer changedfor each wavelength before changing the wavelength. Various orders arepossible and the description and FIG. 8 should not be consideredlimiting.

If a reference channel is used, the same process is carried out for thereference channel—that is (optionally modulated) electromagneticradiation of different wavelengths is emitted at detector without asample or retainer in the path, step 238. The intensity of the receivedelectromagnetic radiation at each wavelength is detected by a detector,step 239, and is passed to the processor 18, and/or database 23, step240. Preferably, each wavelength of electromagnetic radiation can beemitted multiple times, step 241, and the detector intensityoutput/measurement from each is averaged or otherwise processed in theprocessor to obtain the raw training data. Once one wavelength iscomplete, the next wavelength is emitted, step 242. The temperature ateach measurement at each wavelength for the lasers and detectors canalso be taken and passed to the processor/database for storing alongwith the intensity measurement. This is repeated for each sample drug,step 244.

Alternatively, if a monitor diode 4 is used instead of a referencechannel, the same process is undertaken. Optionally modulatedelectromagnetic radiation of different wavelengths is emitted atdetector without a sample or retainer in the path, step 238. Theintensity of the received electromagnetic radiation at each wavelengthis detected by the monitor diode 4, step 239, and is passed to theprocessor 18, and/or database 23, step 240. Preferably, each wavelengthof electromagnetic radiation can be emitted multiple times, step 241,and the monitor diode intensity output/measurement from each is averagedor otherwise processed in the processor to obtain the raw training data.Once one wavelength is complete, the next wavelength is emitted, step242. Each drug can also be tested multiple times at each wavelength in adifferent retainer (e.g. different test tubes) to average out variationsin each retainer, step 243—the monitor diode output is obtained for eachone. The temperature at each measurement at each wavelength for thelasers and detectors can also be taken and passed to theprocessor/database for storing along with the intensity measurement.This is repeated for each sample drug, step 244.

The result is a store of raw training data of (spectral) intensities andtemperatures for each measurement at each wavelength for each sampledrug and for each monitor diode 4 or reference channel measurement. Thedata comprises spectral transmission intensities (in the form describedpreviously) at the wavelengths of interest (e.g. 6 wavelengths) alongwith respective temperature readings for each training drug. Where amonitor diode is used, the data also comprises spectral transmissionintensities at the wavelengths of interest (e.g. 6 wavelengths) for eachtraining drug. Where a reference channel is used, the data alsocomprises spectral transmission intensities at the wavelengths ofinterest (e.g. 6 wavelengths) along with respective temperature readingsfor each reference channel measurement. The raw training data willconsist of multiple scans at each wavelength (typically 25 scans areused although any suitable number can be) using different retainers (forexample, 5 different test tube retainers). The (spectral) intensitiescan take the form of a voltage or similar output from the detector thatis digitised for the processor. In the case of the modulated source(which will be described further below) the digitised intensity may takethe form of a wave form, or the amplitudes of components of the waveform.

The training data is obtained at a measured temperature. For latertemperature compensation, the slope of the intensity versus temperaturefor a sample at a particular wavelength is obtained, step 240. Thishappens by placing the sample under test (preferably in the sameretainer) into a laboratory spectrometer known in the art. The intensityfor each sample is measured at several temperatures for each wavelength,and a straight line slope di/dt of the intensity versus temperaturedetermined and passed to the processor 18/database 23 for later use.

The raw training data is later processed during theverification/identification process to obtain comparison (also termed“training”) coefficients (comparison data) that can be used toverify/identify unknown samples in an actual blind test. In a preferredembodiment, the raw training data has dark current eliminated and istemperature corrected to match the blind sample test temperature. Thedata is converted into a set of coefficients, each of which reducessensitivity to variations in the retainer path length and that isconcentration independent and compensates for variations in the retainerpath length. In a preferred embodiment, this processing occurs at thetime of carrying out the blind test or shortly thereafter, but this isnot essential. The processing could alternatively be carried out inadvance of the actual blind test or after the blind test. The processingof the raw training data is described in detail further below.

Acquisition of Blind Test Data and Verification/Identification

In overview, the blind test data for an unknown sample drug is acquiredas described previously resulting in raw blind test data comprisingintensities and sample temperatures (T_(b)) at various wavelengths asmeasured during the blind test of the actual drug, and also (where used)reference intensities and temperatures at various wavelengths from themonitor diode (or alternatively the reference channel). The blind testraw data is processed to generate blind test (sample) coefficient(s).Mathematical analysis can be carried out between training coefficientsbased on previously determined training/comparison data and blind testcoefficients to identify/verify the unknown sample under test.

In summary, the following occurs to each value of the raw data (eachbeing data representing the detected intensity for a particularwavelength for a particular sample), which initially represents amodulated output from the detector.

-   -   First, the DC component of the output (for the blind test data        and the training data) is removed/eliminated (e.g. utilising a        modulation technique) and the magnitude of the signal is        obtained (see heading—eliminating dark current using        modulation). This DC component elimination occurs on the raw        training data, either at the time of collection, or during the        verification/identification process. This results in:        -   a set of dark current eliminated data (N₁ to N_(n)) (for the            unknown sample under test) comprising a data point for each            wavelength of the blind test data;        -   a set of dark current eliminated data (N₁ to N_(n)) for each            drug in the training set comprising a data point for each            wavelength of the raw training data.    -   Second, the magnitude of the set of dark current eliminated data        (N₁ to N_(n)) for each drug in the training set comprising a        data point for each wavelength of the raw training data then        undergoes temperature correction/adjustment (see        heading—temperature correction). This results in a set of        temperature corrected data (I(T_(b))₁ to/(T_(b))_(n)) for each        drug in the training set that matches the temperature of the        blind test sample.    -   Third, a fractional intensity ratio (fractional spectral        intensity) is obtained for:        -   each dark current eliminated data point (N₁ to N_(n)) for            the unknown sample under test;        -   each dark current eliminated data point (N₁ to N_(n)) for            each drug in the training set    -   wherein the fractional intensity ratio is a parameter that        reduces retainer tolerance sensitivity in verifying/identifying        a substance (see heading—retainer tolerance sensitivity        reduction.

This results in:

-   -   a set of fractional spectral intensity data (g_(m1) to g_(mn))        (for the unknown sample under test) comprising a data point for        each wavelength of the blind test data;        -   a set of fractional spectral intensity data (g_(m1) to            g_(mn)) for each drug in the training set comprising a data            point for each wavelength of the raw training data.    -   Fourth, a coefficient is derived from using the fractional        intensity ratio that is independent of sample concentration (see        heading—concentration independent coefficients).

This results in:

-   -   a set of concentration independent data (y_(m1) to y_(mn)) (for        the unknown sample under test) comprising a data point for each        wavelength of the blind test data;    -   a set of concentration independent data (y_(m1) to y_(mn)) for        each drug in the training set comprising a data point for each        wavelength of the raw training data.        The set of data (y_(m1) to y_(mn)) for the unknown sample under        test can then be compared to set of data (y^(B) _(m1) to y^(B)        _(mn)) for each drug in the training set to verify or identify        the unknown sample under test.

The verification/identification processing will now be described in moredetail, with reference to FIG. 24 that shows step 235 of FIG. 23 in moredetail.

Eliminating Dark Current Using Modulation

First, the dark current of the photodetectors 17/4 is compensated for,step 235 a. This is done for the reference and sample data for both theraw training data and the blind test data. Photodetectors have abaseline output (termed “dark current”) even when there is no incidentradiation. In this embodiment, rather than using a traditional chopperwheel arrangement to find dark current, laser driver current modulationis used to eliminate the need for dark current readings. Referring tothe analyser in FIG. 22, the laser is output is modulated as previouslydescribed, step 230, FIG. 23. The affected detected radiation isreceived by the sample and reference detectors 17 (for both the trainingprocess and the blind test) and passed to the processor 18, steps 231,232 for processing as previously described. The received output at theprocessor contains DC components corresponding to dark current asdemonstrated in the derivation below. This output can be processed bythe processor step 235 a to remove the dark current (DC) componentA_(0S) and A_(0R) of the received output (as per the equations below)and any other unwanted components. The desired components sin(ωt) andcos(ωt) are obtained and represent the intensity measurement withoutdark current. This processing can be done using any suitable signalprocessing know to those in the art.

For example, in one possibility, Fourier analysis of the output currentscould be performed by multiplying the outputs by sin(ωt) and cos(ωt)respectively, and integrating over a period of the oscillation. This canbe used where the modulation is a single frequency, e.g. sine wavemodulation at a single frequency. This procedure provides a form ofaveraging which is beneficial in reducing measurement noise.

Alternatively, a Fast Fourier Transform (FFT) algorithm can be appliedto a digitised output waveform and the relevant Fourier componentsextracted. From the Fourier coefficients we therefore obtain:

S·ΔP=√{square root over (A_(1S) ²+B_(1S) ²)} for the sample channel andR·ΔP=√{square root over (A_(1R) ²+B_(1R) ²)} for the reference channel.

Taking the ratio of these Fourier amplitudes eliminates the dependenceon the modulation depth ΔP to give a normalised (intensity) output, N,given by:

$N = \frac{S}{R}$Where:

-   -   S is a constant representing the attenuation in the optical path        including the sample cell.    -   R is a constant representing the fraction of incident power        delivered to the reference.

A value of N (compensated intensity component) is determined at eachwavelength of interest for the liquid/drug under analysis (be it atraining sample or unknown sample under blind test) The set of valuesfor each wavelength for a drug form a set of dark current eliminateddata (N₁ to N_(n)). For example, where 8 wavelengths are used fortesting, the set will comprise 8 N values—one for each wavelength

The procedure results in dark current eliminated training data(comprising intensity components with the dark current removed) for thesamples/drugs in the training process/set and dark current compensatedblind test data (comprising intensity components with the dark currentremoved) for the sample under blind test. The intensity components withthe dark current eliminated (N₁ to N_(n)) for each drug (in the trainingset and under actual blind test) are stored in the processor 18 and/ordatabase 23.

Derivation of Dark Current Elimination Using Modulation

The modulated affected radiation leaving the sample 16 is detected bythe photodetector 17, which provides a resulting output current. Theoutput current is the sum of two components—a dark current term that ispresent even in the absence of any illumination, and a term proportionalto the intensity of light incident on the detector. Therefore, we canwrite the sample channel output current, I_(S) as follows:I _(S) =I _(S) ^(Dark) +S·P  (1)where in (1):I_(S) ^(Dark) is the dark current signal of the sample channel detectorS is a constant representing the attenuation in the optical pathincluding the sample cell.P is the incident power illuminating the sample cell.

A similar expression can be written for the reference channel outputcurrent, I_(R), generated from the built-in photo-detector of the laserdiode source, namely:I _(R) =I _(R) ^(Dark) +R·P  (2)where in (2):I_(R) ^(Dark) is the dark current signal of the reference photo-detectorin the laser diode package.R is a constant representing the fraction of incident power delivered tothe reference.

The laser 211 output is modulated by modulating the driver current witha known waveform. Typically, a sinusoidal modulation with angularfrequency ω is used to vary the current about a mean value. This has theeffect of modulating the output power of the laser diode source in asimilar sinusoidal manner illustrated in FIG. 17:

Mathematically, the time-dependent laser output power, P(t), can bewritten as follows:P(t)=P ₀ +ΔP·sin(ωt+ϕ)  (3)where in (3):P₀ is the mean output power from the laserΔP is the modulation amplitude in the output power waveform (depth ofmodulation)ϕ is the phase of the modulation waveform at tine, t=0.

Substituting for the incident power in equations (1) and (2) using (3),the following expressions for the output currents from sample andreference channels are obtained:I _(S) =I _(S) ^(Dark) +S·P ₀ +S·ΔP·sin(ωt+ϕ)I _(R) =I _(R) ^(Dark) +R·P ₀ +R·ΔP·sin(ωt+ϕ)

The parameters of interest with respect to characterising the sampleunder test are the constants S and R. The ratio of these two constantsrepresents a normalised coefficient characteristic of the liquid in thesample cell.

Expanding the sinusoidal term in the above equations, gives:sin(ωt+ϕ)=sin(ωt)cos ϕ+cos(ωt)sin ϕwhich gives the following:

$\begin{matrix}\begin{matrix}{I_{S} = {I_{S}^{Dark} + {S \cdot P_{0}} + {{S \cdot \Delta}\;{P \cdot {\sin( {\omega\; t} )}}\cos\;\phi} + {{S \cdot \Delta}\;{P \cdot {\cos( {\omega\; t} )}}\sin\;\phi}}} \\{\equiv {A_{0S} + {A_{1S}{\cos( {\omega\; t} )}} + {B_{1S}{\sin( {\omega\; t} )}}}}\end{matrix} & (4) \\\begin{matrix}{I_{R} = {I_{R}^{Dark} + {R \cdot P_{0}} + {{R \cdot \Delta}\;{P \cdot {\sin( {\omega\; t} )}}\cos\;\phi} + {{R \cdot \Delta}\;{P \cdot {\cos( {\omega\; t} )}}\sin\;\phi}}} \\{\equiv {A_{0R} + {A_{1R}{\cos( {\omega\; t} )}} + {B_{1R}{\sin( {\omega\; t} )}}}}\end{matrix} & (5)\end{matrix}$So that:A _(0S) =I _(S) ^(Dark) +S·P ₀A _(0S) =I _(S) ^(Dark) +S·P ₀A _(1S) =S·ΔP·sin ϕB _(1S) =S·ΔP·cos ϕA _(1R) =R·ΔP·sin ϕB _(1R) =R·ΔP·cos ϕ

Inspection of equations (4) and (5) shows the output currents have theform of a simple Fourier series consisting of constant DC terms, A_(0S)and A_(0R), plus sine and cosine terms that oscillate with themodulation frequency, ω, with amplitudes A_(1S), A_(1R), B_(1S) andB_(1R).

The dark current terms contribute only to the DC term of the Fourierseries in (4) and (5). The dark current terms are contained within theDC components of equations (4) and (5). Therefore, a simple Fourieranalysis of the modulated output waveform gives the Fourier coefficientsof the sin(ωt) and cos(ωt) terms—which are independent of the darkcurrent.

By measuring the sinusoidally varying component of each output current,the constants, S and R, can be determined without the need to measurethe dark current of each detector diode. These latter terms can beeliminated from the measurement by DC blocking components or byperforming a Fourier analysis of the output currents and discarding allbut the sinusoidal terms.

In conventional spectrometer systems, the dark current would be measuredby blocking off the illumination to the detector diode using a rotatingmechanical chopper that periodically blocks then re-instates the opticalillumination. Using the laser-current modulation described aboveeliminates the need for mechanical components such as rotating chopperswhich simplifies the spectrometer design, reduces cost and improvesreliability by not using any moving parts.

Electrical interference from the electric motors used to drivemechanical choppers is also eliminated.

Temperature Correction

In overview, next temperature correction processing can be done tocompensate for changes in intensity measurements from the detector dueto temperature fluctuations of the retainer/sample step 235 b of FIG.24.

It can be shown that the temperature dependence is linear with respectto changes in sample temperature—see further the explanation below.Therefore, for each wavelength, the gradient of the intensity value withrespect to temperature provides information to characterise the changein transmission intensity with changes in temperature. This gradientdata is obtained as described earlier and stored in the drug/dilutantdata base along with the spectral training data.

Using the temperature dependence data in the training set data base (thegradient of intensity with respect to temperature; one gradient for eachwavelength for each undiluted drug), the processor generates a newtraining data set for all undiluted drugs and dilutants at the sametemperature as the blind test sample was measured at, namely, T_(b).This temperature correction is applied to data for all retainers (e.g.test tubes) in the original reference training data set (which has haddark current eliminated as above). This results in a set of temperaturecorrected training data that are the next step in obtaining thecomparison coefficients for verifying/identifying the unknown sampleunder test. Temperature correction applied to the training data set inthis manner allows a direct comparison to be made with data acquired forthe blind test since all data is now converted to/valid at the blindtest sample temperature, T_(b).

As previously described, when performing a blind test on an unknown drugsample, intensity data is measured at different wavelengths and thetemperature is taken of the sample and also stored in the database 23.With the temperature of the fluid known, a set of temperature-correctedtraining data coefficients is generated for all drugs in the data basecorresponding to the temperature of the unknown drug measured in theblind test. Therefore, both the blind test concentration-independentcoefficients and those of the training data set have a commontemperature.

Referring to FIG. 24, step 235 b, temperature compensation occurs asfollows. For each drug in the training set, the set of training data istaken and for each training data value (with dark current eliminated) N₁to N_(n) at each wavelength, the dark current corrected intensity valueis then corrected for temperature using the following equation in theprocessor 18:

$\begin{matrix}{{I( T_{t} )} = {{I( T_{b} )} + {\frac{dI}{dT}\Delta\; T}}} & (6)\end{matrix}$Where in (6),I is the intensity of affected electromagnetic radiation detected by adetector at a particular wavelength for a sample (with dark currenteliminated e.g. N),T_(t) is the temperature of the training sample when the affectedelectromagnetic radiation was detected at that wavelength,T_(b) is the temperature of the unknown sample when the affectedelectromagnetic radiation was detected at that wavelength,ΔT=T_(t)−T_(b) is the sample temperature difference between the trainingsample temperature and unknown sample temperature, and

$\frac{dI}{dT}$is the slope of the linear relationship of between measure intensity andtemperature for a sample at a given wavelength.

All parameters are known from the training data and blind test data.

In particular, the equation is rearranged to solve for I(T_(b)):I(T_(b))=I(T_(t))−(dI/dT)ΔT. Each intensity I(T_(t)) is obtained (beingthe intensity of the training sample obtained during training) alongwith

$\frac{dI}{dT}$and ΔT. For each intensity I(T_(t)) from the training data, acorresponding a temperature corrected I(T_(b)) is obtained using therearranged equation (6) and stored—this correlating to an “expected”intensity for the unknown drug at the blind test temperature if theunknown drug were the training drug. I(T_(b)) is the temperaturecorrected intensity. This corrected I(T_(b)) is what is used tocalculate the training coefficients below.

The temperature correction is not applied to the reference data if itcomes from the monitor diode 4. However, if it comes from a referencechannel with components and/or a sample the temperature correction doestake place as described above.

After this step, the processor 18/database 23 now has a set of trainingdata (I(T_(b))₁ to I(T_(b))_(n)) for each drug in the training set thatrepresents intensities that have had dark current eliminated and havebeen temperature corrected to match the temperature of the unknownsample under test.

Derivation of Temperature Correction

When performing a blind test on an unknown drug sample, intensity datais measured at different wavelengths and the temperature of the sampleare stored. With the temperature of the fluid known, a set oftemperature-corrected training data coefficients is generated for alldrugs in the data base corresponding to the temperature of the unknowndrug measured in the blind test. Therefore, both the blind testconcentration-independent coefficients and those of the training dataset have a common temperature.

The temperature correction is implemented by exploiting theexperimentally observed linear relationship between measured intensityand temperature for a given drug at a particular wavelength as set outbelow. Thus, the temperature dependence of a given drug can be measuredand characterised by a single coefficient at each wavelength of interestwhich corresponds to the slope of the measured intensity with respect totemperature change.

For a given drug in the training data set, at a given wavelength, we canexpress the intensity at temperature T₀+ΔT in terms of that attemperature, T₀ as follows:

$\begin{matrix}{{I( {T_{0} + {\Delta\; T}} )} = {{I( T_{0} )} + {\frac{dI}{dT}\Delta\; T}}} & ({A6})\end{matrix}$Equation (A6) is equivalent to equation (6)

In (A6), the slope

$\frac{dI}{dT}$is a constant coefficient that is known for each drug in the data base.

These coefficients are determined by measurement on each dilutant andundiluted drug of interest. There is a separate coefficient for eachwavelength. These temperature coefficients form part of the trainingdata set.

The temperature T₀ in (A6) is defined as the temperature at which theoriginal training data measurements were performed (as determined fromthe temperature sensor in the fluid test cell holder). This need not bethe same for each entry in the data base, and can be different for eachwavelength.

The temperature deviation from T₀ is denoted by ΔT. This is determinedby measuring the fluid temperature of the unknown drug under test (theblind test) and subtracting the known value of T₀. Thus, atemperature-corrected set of concentration-independent trainingcoefficients can be generated at the same temperature as the blind testmeasurement using the linear correction formula of (A6).

Retainer Tolerance Sensitivity Reduction

The sample retainer 16 could be a test tube, cell, IV line, syringe orother suitable retainer having a transparent wall. Inaccuracies due totolerances in the sample retainer wall and path length and any othergeometric and/or material parameters can be reduced. For example, duringblind or training tests, the fluid (sample) thickness is controlled byhaving a fixed cavity bounded by two optically transparent wallstypically made of a plastic. In the present invention, such plasticretainers are designed to be a consumable product that is used just onceprior to disposal. Although well-controlled during the manufacturingprocess, inevitably there are minor deviations from the intended nominalfluid thickness from tube to tube due to manufacturing tolerances.Typically, for a nominal fluid thickness of e.g. several mm there willbe a dimensional tolerance of +/−15 microns. This dimensionaluncertainty from tube to tube translates into a spread in measuredintensity values for a given fluid around the mean value associated witha retainer of nominal thickness.

In overview, in order to reduce the sensitivity of the intensity data tovariations in the retainer (e.g. test tube) geometry (due tomanufacturing tolerances) the following ‘retainer correction’ algorithmhas been found to work well which generates normalised ratios of theintensity values for the training data and blind test data, step 235 c.This algorithm is applied to both training data and blind test dataafter dark current and temperature correction has been applied. Detailsof the derivation of this algorithm are set out below. While notnecessarily a correction as such, the algorithm produces coefficientsthat render the verification/identification process less sensitive toretainer tolerance/variations.

The process will now be described in more detail with reference to FIG.8, step 235 c. The ratio of the sample intensity to the referenceintensity for each wavelength (from the compensated training data orblind test data as appropriate) is then evaluated by the processor, foreach retainer (in the case where multiple tests are carried out on thesame sample in multiple retainers). Second, the ratio data is normalisedby the processor for each retainer with respect to thesum-over-wavelengths. Mathematically, this is described below.

Firstly, for each undiluted drug and dilutant in the training set, foreach wavelength, the average is found over the number of scans for theset of reference raw data intensities (however obtained, e.g. by monitordiode or via a reference channel) and the set of training data rawintensities ((I(T_(b))₁ to I(T_(b))_(n)) in the case where both thereference and training data set have been processed for dark current andtemperature correction as described earlier). The ratio f_(m) of theseaverages (being training raw data average intensities divided by thereference raw data average intensities) is found for each wavelength,for each retainer. Secondly, the ratio data is normalised for each tubewith respect to the sum-over-wavelengths. Mathematically, this isdescribed below.

Denoting the ratio at the m^(th) wavelength by f_(m), the normalisedratios are given by the parameter g_(m) as follows:

$\begin{matrix}{g_{m} = \frac{f_{m}}{\Sigma\; f_{m}}} & (7)\end{matrix}$

The same is also carried out for the sample data as required. That is,for the sample drug and dilutant, for each test wavelength, the averageis found over the number of scans for the reference raw data intensities(however obtained, e.g. by monitor diode or via a reference channel) andthe unknown sample data raw intensities (both of which may have beenprocessed for dark current and temperature correction). The ratio f_(m)of these averages (unknown sample raw data average intensities dividedby the reference raw data average intensities) is found for each (test)wavelength. Secondly, the ratio data is normalised for each tube withrespect to the sum-over-wavelengths. Mathematically, this is describedin equation (7) above.

The g_(m) values represent the fractional (spectral) intensity (alsotermed “fractional ratio”) defined as the proportion of transmittedlight measured at the m^(th) wavelength referenced to the sum ofintensities over all test wavelengths measured for a given retainer. Thevalues of g_(m) always lie between 0 and 1 since they representfractions of the total amount of energy received over all testwavelengths measured.

For the temperature corrected and dark current eliminated training data,a set of g_(m) values (g_(m1) to g_(mn)) for each retainer used isobtained as per equation (7). The same procedure is applied to the darkcurrent corrected blind test data to obtain a set of values (g_(m1) tog_(mn)) for the retainer used.

This results in a set of g_(m) coefficients (g_(m1) to g_(mn)) for eachdrug, that are stored in the processor 18/database 23 and that form thebasis of training and blind test coefficients that can be calculated (asset out further below) that can be used for verification/identificationpurposes with reduced sensitivity to retainer geometry.

Derivation for Retainer Tolerance Sensitivity Reduction

Measurements of intensity for the fluid under test are carried out usinga purpose-made test tube (vial) which contains the fluid sample. Thefluid thickness is controlled by having a fixed cavity bounded by twooptically transparent walls typically made of a plastic. A typical fluidthickness is several mm with the plastic walls having a comparable totalthickness. In the present invention, such plastic test tubes aredesigned to be a consumable product that is used just once prior todisposal.

Although well-controlled during the manufacturing process, inevitablythere are minor deviations from the intended nominal fluid thicknessfrom tube to tube due to manufacturing tolerances. Typically, for anominal fluid thickness of several mm there will be a dimensionaltolerance of +/−15 microns for an injection-moulded component. Thisdimensional uncertainty from tube to tube translates into a spread inmeasured intensity values for a given fluid around the mean valueassociated with a tube of nominal thickness. This error can be expressedmathematically for the m^(th) wavelength in the measurement set usingthe Beer-Lambert law as a starting point, namely:

$\begin{matrix}{f_{m} = {( \frac{I}{I_{0}} )_{m} = {T_{m}e^{{- 2}{\overset{\_}{\alpha}}_{m}w}e^{{- 2}\;\alpha_{m}d}}}} & ({B1})\end{matrix}$where in (B1):Measured transmitted intensity through the fluid in its test tube (inthis case temperature corrected I(T_(b))).I₀=Incident intensity on the test tube (proportional to the referencechannel reading).T_(m)=Transmission factor involving the refractive indices of the testtube wall and fluid that for reflections at the material interfaces.α _(m)=Attenuation coefficient of test tube wall material with totalthickness, w, at wavelength.α_(m)=Attenuation coefficient of fluid with thickness, d, at m^(th)wavelength.

By way of example, consider the sensitivity of the measured transmissioncoefficient f_(m) with respect to changes in the fluid thickness, d.Differentiating (B1) with respect to d while keeping all other variablesconstant gives:

$\begin{matrix}{\frac{\partial f_{m}}{\partial d} = {{- 2}\alpha_{m}f_{m}}} & ({B2})\end{matrix}$

Defining the nominal fluid thickness as d₀ and the deviation from thisvalue as Δd, the resulting effect on the measured intensity can beexpressed as:

$\begin{matrix}{{f_{m}( {d_{0} + {\Delta\; d}} )} = {{f_{m}( d_{0} )} + {\frac{\partial f_{m}}{\partial d}\Delta\; d}}} & ({B3})\end{matrix}$Combining (B2) and (B3) gives the error term as:Δf _(m) =f _(m)(d ₀ +Δd)−f _(m)(d ₀)=−2α_(m) f _(m) Δd  (B4)

Measurements carried out on fluids using numerous test tubes of the samenominal design have shown that errors of the form given in (B4) areconsistent with the typical dimensional tolerance associated with thefluid space, d. It has also been found that these tube-to-tubevariations can be comparable or larger in magnitude than the differencebetween mean intensity values between some drugs. This makes drugdiscrimination for certain drugs very difficult or even impossible.

To remedy this, an alternative measurement parameter is considered whichis less sensitive to the dimensional tolerances associated with the testtubes. This parameter is the fractional intensity, denoted by g_(m),which is defined as the proportion of transmitted light measured at thewavelength referenced to the sum of intensities over all wavelengthsmeasured for a given test tube. That is we define:

$\begin{matrix}{g_{m} = \frac{f_{m}}{\sum f_{m}}} & ({B5})\end{matrix}$

The values of g_(m) always lie between 0 and 1 since they representfractions of the total amount of energy received over all wavelengthsmeasured. To estimate the sensitivity of g_(m) to dimensional tolerancesin the fluid thickness, we follow a similar procedure to before usingpartial differentiation with respect to the fluid thickness, d. DenotingΣf_(m) by Σ, this gives:

$\begin{matrix}{\frac{\partial g_{m}}{\partial d} = {{\frac{1}{\Sigma}\frac{\partial f_{m}}{\partial d}} - {\frac{f_{m}}{\sum^{2}}\frac{\partial\Sigma}{\partial d}}}} & ({B6})\end{matrix}$Using (B6) and noting that, using (B2),

$\frac{\partial\Sigma}{\partial d} = {{- 2}{\Sigma\alpha}_{m}f_{m}}$we can express the error term Δg_(m) associated with g_(m) in thefollowing form:

$\begin{matrix}{{\Delta\; g_{m}} = {{{g_{m}( {d_{0} + {\Delta\; d}} )} - {g_{m}( d_{0} )}} = {\frac{\Delta\; f_{m}}{\Sigma} - {\frac{f_{m}}{\Sigma^{2}}{\sum_{m}{\Delta\; f_{m}}}}}}} & ({B7})\end{matrix}$

Inspection of (B7) indicates that the spread in values, Δg_(m),associated with dimensional tolerances in the fluid thickness in thetest tube are reduced in magnitude when we use the fractional intensityg_(m) instead of the transmission coefficient f_(m). This is by virtueof the denominator terms in (B7) which involve the factors Σ and Σ²which are larger than unity. We now consider the case of the fractionalintensity parameter for a test tube of nominal fluid thickness d₀ whenthe fluid attenuation coefficient changes, as would occur whenperforming measurement s on different drugs. If the fluid attenuationcoefficient changes from α_(m) to α_(m)+Δα_(m) then the effect on thefractional intensity is as follows:

$\begin{matrix}{{g_{m}( {\alpha_{m} + {\Delta\;\alpha_{m}}} )} = {\frac{f_{m}( {\alpha_{m} + {\Delta\;\alpha_{m}}} )}{\Sigma\;{f_{m}( {\alpha_{m} + {\Delta\;\alpha_{m}}} )}} = \frac{{f_{m}( \alpha_{m} )}( {1 - {2d_{0}\Delta\;\alpha_{m}}} )}{\sum{{f_{m}( \alpha_{m} )}( {1 - {2d_{0}\Delta\;\alpha_{m}}} )}}}} & ({B8})\end{matrix}$In (B8), variations in attenuation coefficient in the denominator willbe negligible compared to those in the numerator. Therefore, we canwrite (B8) as:

$\begin{matrix}{{{g_{m}( {\alpha_{m} + {\Delta\alpha}_{m}} )} \cong \frac{{f_{m}( \alpha_{m} )}( {1 - {2d_{0}\Delta\;\alpha_{m}}} )}{\Sigma\;{f_{m}( \alpha_{m} )}}} = {{g_{m}( \alpha_{m} )}( {1 - {2d_{0}\Delta\;\alpha_{m}}} )}} & ({B9})\end{matrix}$Therefore, from (B9), the fractional change in the parameter g_(m) withrespect to changes in the fluid attenuation coefficient is given by:

$\begin{matrix}{\frac{{g_{m}( {\alpha_{m} + {\Delta\;\alpha_{m}}} )} - {g_{m}( \alpha_{m} )}}{g_{m}( \alpha_{m} )} \cong {{- 2}d_{0}\Delta\;\alpha_{m}}} & ({B10})\end{matrix}$

Equation (B10) establishes that the fractional intensity parameter g_(m)remains sensitive to changes in fluid attenuation coefficient and so issuitable as a drug discrimination parameter. The use of the fractionalintensity parameter g_(m) has been tested with measured data obtainedusing multiple test tubes containing the same fluid. The resultingspread in values across different tubes was found to be greatly reducedcompared to values obtained using just the measured transmissioncoefficients, thereby verifying the theoretical result of (B7).

It was also found that the inherent differences in the attenuationcoefficients of different drugs were still maintained when using thefractional intensity parameter, which verified the result of (B10).

The reduction in sensitivity to fluid thickness variations from testtube to test tube proved to be a key factor in discriminating betweendrugs which had previously proven impossible to tell apart from just thetransmission coefficient data alone.

Concentration Independent Coefficients

Having applied dark current, temperature correction and path lengthcorrection as described above resulting in the g_(m) coefficients fromthe training data and blind test data, the next step is from that togenerate a set of spectral (comparison) coefficients at each wavelengthfor a given drug-dilutant combination that are independent ofconcentration, step 235 d, for both the training sample drugs andunknown sample drug under blind test.

It has been shown experimentally and theoretically that the intensityfor a given drug-dilutant combination is linearly dependent on theconcentration. Consequently, it is possible to characterise thisdependence using the slope of the resulting straight line with respectto the volume fraction of undiluted drug, denoted by x. Here, x=0corresponds to the case of pure dilutant, and x=1, the case for the pureundiluted drug. Details of the derivation of theconcentration-independent coefficients are set out below.

Referring to FIG. 24, step 235 d, the steps involved in calculating inthe processor the concentration-independent coefficients are givenbelow: First choose a dilutant—for example, 0.9% saline. Next, from thecompensated training data set obtain/evaluate, as set out previously,the average-over-test-tubes for the g_(m) values for the chosen dilutantand for each undiluted drug. There will be one such average value foreach drug and the chosen dilutant for each wavelength (suffix m). Denotethe undiluted drug tube averages by g_(m) and those of the dilutant asg_(m) ⁰ . For the blind test data, (for which there is only a singleretainer), also obtain/evaluate value denoted by g_(m) ^(B)(x) where xis the unknown concentration (superscript B for blind test). Next, foreach drug, subtract the dilutant tube average from each undiluted drugtube average, to give the slope, s_(m), of the intensity versusconcentration curve for each drug-dilutant combination, that is:s _(m)= g _(m) − g _(m) ⁰   (8)

Next, the processor carries out the same steps for each furtherdilutant. Next, the processor evaluates the training-set coefficientsy_(m) as follows:

$\begin{matrix}{y_{m} = \frac{s_{m}}{\sqrt{\Sigma_{m}s_{m}^{a}}}} & (9)\end{matrix}$

These coefficients are the slopes of equation (8) normalised withrespect to the root-sum-of-squares taken over all wavelengths. Thesecoefficients are independent of the concentration x and are defined ateach wavelength for a given undiluted drug and its chosen dilutant.

Next, we now turn our attention to the blind data for which the drugidentity and concentration are both unknown. For the case of a mixtureof drug and chosen dilutant with unknown concentration, x, the lineardependence on concentration for the spectral intensity, g_(m) ^(B)(x),at the m^(th) wavelength can be defined by the following:g _(m) ^(B)(x)− g _(m) ⁰ = s _(m) ^(B) x   (10)

-   -   Where in equation (10) the concentration slope for the blind        test drug is denoted by s_(m) ^(B) which, along with the        concentration, x, is unknown.

Using equation (10) and the equation given in (9), a set ofconcentration-independent coefficients for the blind test drug, y_(m)^(B), can be evaluated by the processor as follows:

$\begin{matrix}{y_{m}^{B} = {\frac{s_{m}^{B}}{\sqrt{{\Sigma_{m}( s_{m}^{B} )}^{2}}} \equiv \frac{{g_{m}^{B}(x)} - \overset{\_}{g_{m}^{0}}}{{\Sigma_{m}( {{g_{m}^{B}(x)} - \overset{\_}{g_{m}^{0}}} )}^{2}}}} & (11)\end{matrix}$

Equation (11) shows that the above coefficients y_(m) ^(B) can bedetermined from the measured values of g_(m) ^(B)(x) and the knowndilutant values g_(m) ⁰ obtained from the training data set.

Since there will be several possible dilutants used, if the identity ofthe dilutant is not known or in doubt, the above procedure can berepeated for each different dilutant giving rise to a different set ofconcentration-independent coefficients for both training data and blindtest data. The full set of y_(m) and y_(m) ^(B) would therefore, ingeneral, consist of coefficients for all dilutants of interest.

Using the equations 9 and 11, the processor obtains y_(m) resulting in aset (y_(m1) to y_(mn)) of training data coefficients for each drug, andy_(m) ^(B) resulting in a set (y^(B) _(m1) to y^(B) _(mn)) of blind testdata coefficients (together “comparison coefficients”), which are storedin the processor 18/database and can be used for verificationidentification.

Derivation of Concentration Independent Coefficients

The properties of each fluid of interest can be characterised by itscomplex refractive index. We can write the complex refractive index ofthe fluid under test, n, in terms of its real and imaginary parts n′ andn″ as:n=n′−jn″  (C1)where in (C1): √{square root over (j=−1)}.

Physically, the real part of the refractive index, determines thewavelength of electromagnetic radiation in the fluid according toλ=λ₀/n′ where λ_(n) is the wavelength in free space. More importantlyfor NIR transmission through aqueous fluids, the imaginary part of therefractive index, n″, determines the attenuation (via absorption) ofincident electromagnetic waves consistent with the Beer-Lambert law asfollows:

$\begin{matrix}{\frac{I}{I_{0}} = e^{{- 2}\alpha\; d}} & ({C2})\end{matrix}$

In (C2), the transmitted light intensity through the fluid is denoted byI with I₀ the intensity incident on the fluid sample. The thickness ofthe fluid is denoted by d and α is the attenuation coefficient which isgiven by:

$\begin{matrix}{\alpha = {\frac{2\pi}{\lambda_{0}}n^{''}}} & ({C3})\end{matrix}$Therefore, the measured attenuation through a fluid under test at agiven free-space wavelength is determined by the imaginary part of thecomplex refractive index of the fluid. A common occurrence in thepreparation of intravenous drugs prior to administration, is dilution ofa drug with a dilutant such as saline or water. Drug verification underthese circumstances has the additional complication of drugconcentration which needs to be accounted for in any subsequentverification analysis. The following procedure is applied to obtain aset of coefficients for each undiluted drug that is independent of thedrug's concentration when the identity of the dilutant is known.

Consider the diluted drug as a mixture of two fluids, each denoted bysubscripts ‘1’ and ‘2’, with complex relative permittivities ϵ₁ and ϵ₂,respectively. The complex relative permittivity of the fluid under testis denoted by ϵ and is related to the complex refractive index, n, ofthe fluid by the relation:ϵ=n ²  (C4)

This complex relative permittivity can be expressed in terms of thecomplex relative permittivities of the individual components and thevolume fraction of each component by invoking the Lichtenecker mixturelaw [ref 1] which is given below:

$\begin{matrix}{\frac{\epsilon}{\epsilon_{1}} = ( \frac{\epsilon_{3}}{\epsilon_{1}} )^{x}} & ({C5})\end{matrix}$

In (C5), x denotes the volume fraction of component ‘2’ which we candefine as the undiluted drug, with component ‘1’ the dilutant. Thus,when x=0, the mixture consists of 100% dilutant, and when x=1, themixture is 100% undiluted drug.

Until recently, this formula was regarded as semi-empirical in naturewithout any firm physical basis. However, in 2010, the formula wasderived from first principles by Simpkin [ref 2] using Maxwell'sequations and the conservation of charge.

Equation (C5) can now be expressed in terms of the complex refractiveindices of the relevant media by substituting (C4) into (C5) and takingthe square root of each side. This gives the self-same formula for thecomplex refractive indices of the mixture, namely:

$\begin{matrix}{\frac{n}{n_{1\;}} = ( \frac{n_{2}}{n_{1}} )^{x}} & ({C6})\end{matrix}$

Where n₁ is the complex refractive index of the dilutant and n₂ is thecomplex refractive index of the undiluted drug with volume fraction x.

We now express the complex refractive index of the undiluted drug interms of the difference, Δn, with respect to that of the dilutant, thatis:n ₂ =n ₁ +Δn  (C7)

Substituting (C7) into (C6) gives:

$\begin{matrix}{\frac{n}{n_{1\;}} = ( {1 + \frac{\Delta\; n}{n_{1}}} )^{x}} & ({C8})\end{matrix}$

For the case of intravenous drugs, the complex refractive index isdominated by the properties of water and deviations in complexrefractive index from that of water are small in magnitude. Therefore,in (C8), the fraction Δn/n₁ is small compared with unity so that to avery good approximation we can expand the right hand side in a Binomialseries and use only the first few terms. Thus, (C8) becomes:

$\begin{matrix}{ {\frac{n}{n_{1}} \cong {1 + {x\;\frac{\Delta\; n}{n_{1}}}}}\Rightarrow{n \cong {n_{1} + {x\;\Delta\; n}}}  = {n_{1} + {x( {n_{2} - n_{1}} )}}} & ({C9})\end{matrix}$

Therefore, the mixture law for the two fluids is well-approximated by alinear relationship with respect to the volume fraction of the undiluteddrug. Taking the imaginary part of both sides of (C9) then gives:n″=n″ ₁ +x(n″ ₂ −n″ ₁)  (C10)

If we now take the natural logarithm of the Beer-Lambert law of equation(C2) and substitute for the attenuation coefficient α using (C3), weobtain:

$\begin{matrix}{{{- \ln}\mspace{11mu}( \frac{I}{I_{0}} )} = {{2\alpha\; d} = {\frac{4\pi\; d}{\lambda_{0}}n^{n}}}} & ({C11})\end{matrix}$

If I represents the measured transmitted intensity of a diluted drug,then we can substitute for n″ using equation (C10) to obtain thefollowing:

${{- \ln}\mspace{11mu}( \frac{I}{I_{0}} )} = {\frac{4\pi\; d}{\lambda_{0}}\{ {n_{1}^{n} + {x( {n_{2}^{n} - n_{1}^{n}} )}} \}}$

The above expression can be expressed as follows:

$\begin{matrix}{{\ln\mspace{11mu}( \frac{I}{I_{0}} )} = {{( {1 - x} )\mspace{11mu}\ln\mspace{11mu}( \frac{I_{1}}{I_{0}} )} + {x\mspace{11mu}\ln\mspace{11mu}( \frac{I_{2}}{I_{0}} )}}} & ({C12})\end{matrix}$where in (C12):

In

$( \frac{I_{1}}{I_{0}} ) = {{- \frac{4\pi\; d}{\lambda_{0}}}n_{1}^{n}}$is the Beer-Lambert law applicable to the pure dilutant with measuredintensity I₁, and

In

$\;{( \frac{I_{2}}{I_{0}} ) = {{- \frac{4\pi\; d}{\lambda_{0}}}n_{2}^{n}}}$is the Beer-Lambert law applicable to the undiluted drug with measuredintensity I₂.

The above can be further simplified since the incident intensity I₀cancels out in (C12) to give:

$\begin{matrix}{{\ln\mspace{11mu}( \frac{I}{I_{1}} )} = {x\mspace{11mu}\ln\mspace{11mu}( \frac{I_{0}}{I_{1}} )}} & ({C13})\end{matrix}$

Equation (C13) shows that the measured intensities obey a logarithmicmixture law identical to the Lichtenecker formula. Expressions likethose in (C13) can be applied to a given drug-dilutant mixture for eachof several wavelengths measured.

In (C13), we can simplify the logarithmic expressions by observing thatthe measured spectral intensities differ only slightly for differentdrugs. That is, the ratios

$( \frac{I}{I_{2}} )\mspace{14mu}{and}\mspace{14mu}( \frac{I_{2}}{I_{1}} )$are close to unity. Therefore, we can write the followingapproximations:

${\ln\mspace{11mu}( \frac{I}{I_{1}} )} = {{\ln\mspace{11mu}( {1 + \frac{( {I - I_{1}} )}{I_{1}}} )} \cong \frac{( {I - I_{1}} )}{I_{1}}}$and${\ln\mspace{11mu}( \frac{I_{2}}{I_{1}} )} = {{\ln\mspace{11mu}( {1 + \frac{( {I_{2} - I_{1}} )}{I_{1}}} )} \cong \frac{( {I_{2} - I_{1}} )}{I_{1}}}$which are valid since I−I₁ and I₂−I₁ are small in magnitude with respectto I₁. Using these approximations in (C13) results in the followinglinear expression:I(x)−I ₁=(I ₂ −I ₁)x  (C14)

Expressions of the form given in (C14) can be defined for eachwavelength. The important point to note is that the volume fraction ofthe undiluted drug, x, which is a measure of the drug concentration, iscommon to all wavelengths for a given mixture. Therefore, by makingmeasurements at a minimum of two wavelengths, it is possible toeliminate the concentration, x, and obtain values that arecharacteristic of the particular undiluted drug with respect to a givendilutant. The optimum way to eliminate the concentration, x, thatutilises measured data at all wavelengths, is proposed as follows. Anormalisation procedure is used whereby the normalising factor is theroot-sum-of-squares over all wavelengths. To illustrate this latterscheme, consider M wavelengths so that we obtain a set of M equationslike that in (C14), one for each wavelength, λm, where m=1, 2, 3 . . .M, namely:I(x,λ _(m))−I ₁(λ_(m))=(I ₂(λ_(m))−I ₁(λ_(m)))x  (C15)

In (C15) we now square both sides, sum over all wavelengths (suffix m)and take the square root to give the following expression for x:

$\begin{matrix}{x = \sqrt{\frac{\sum_{m}( {{I( {x\;,\lambda_{m}} )} - {I_{1}( \lambda_{m} )}} )^{2}}{\sum_{m}( {{I_{2}( \lambda_{m} )} - {I_{1}( \lambda_{m} )}} )^{2}}}} & ({C16})\end{matrix}$

Substituting for x in (C14) using (C16) then gives for each wavelength acoefficient, y_(m) defined as follows:

$\begin{matrix}{y_{m} = {\frac{{I( {x,\lambda_{m}} )} - {I_{1}( \lambda_{m} )}}{\sqrt{\sum_{m}( {{I( {x,\lambda_{m}} )} - {I_{1}( \lambda_{m} )}} )^{2}}} = \frac{{I_{2}( \lambda_{m} )} - {I_{1}( \lambda_{m} )}}{\sqrt{\sum_{m}( {{I_{2}( \lambda_{m} )} - {I_{1}( \lambda_{m} )}} )^{2}}}}} & ({C17})\end{matrix}$

By virtue of the far right-hand side of (C17), the coefficients y_(m)are independent of the drug concentration and are characteristic of theundiluted drug and its dilutant.

When performing a blind test on an unknown drug, the coefficients arefound by measuring the intensity I(x,λ_(m)) for the unknown drug mixtureat M wavelengths. For each wavelength, the difference between thesemeasured intensities and the dilutant is then normalised with respect tothe root-sum-over-squares over all wavelengths as per the firstexpression on the right hand side of (C17). The identity of the dilutantis assumed known and its intensity I₁(λ_(m)), which will typically becontained within the set of training data. Usually, the dilutant issaline, water, or glucose. If the dilutant identity is not known, or isin doubt, concentration-independent coefficients for all possiblecombinations of dilutants and undiluted drugs can be determined for usein the drug verification analysis.

The consequence of (C17) is that when generating a set of training data,it is only necessary to measure the intensities of the dilutants ofinterest (denoted by I₁(λ_(m)) in (C17) and the intensities of the drugsof interest in their undiluted form (denoted by I₂(λ_(m)) in (C17). Itis not necessary to generate training data for every possiblecombination of dilutant and drug—just data for each dilutant and eachundiluted drug of interest. The set of training data for a range ofdrugs and dilutants is then populated by concentration-independentcoefficients given by the far right-hand side of (C17).

Once a drug's identity has been verified from the blind test andtraining set coefficients so generated, it is possible to determine theconcentration of the drug by calculating the value of x byback-substitution using (C15).

Drug Verification/Identification or Other Characterisation

Now that there exists a set of concentration-independent coefficientsfor each of the drugs in the training data set with its chosendilutant—these are the training coefficients y_(m) obtained fromequation (9). Now there also exists a set of concentration-independentcoefficients for the unknown blind test drug—these are the samplecoefficients y_(m) ^(B) obtained from equation (11).

The drug identity is now verified/identified or otherwise characterisedby the processor using, for example, Linear Discriminant Analysis, withy_(m) as training data and y_(m) ^(B) as test data, step 235 e. Ingeneral terms, the representative sample/training coefficients are foundfor the sample at each selected wavelength and with respect to eachother comparison sample. The sample coefficients are analysed againstthe training coefficients. Representative value(s) could be obtained foreach sample based on the coefficients. If there is sufficient similaritybetween the representative value(s) found for the unknown sample and therepresentative value(s) of a training sample (corresponding to the samesample), then verification or identification is made. Sufficientsimilarity can be determined using any suitable statistical or othertechnique. For example, sufficient similarity might occur when some orall of the representative values match those in the verification matrix.In another example, this might occur when the sample falls below thethreshold for each comparison sample. An alarm or output might be madevia a user interface to advise the user of the result of theverification/identification.

In verification, the y_(m) ^(B) values for the unknown sample areanalysed against the y_(m) values for the drug identified/entered by theclinician to see if there is a match. An output answer such as “Yes” or“no” can be output on the interface to advise the clinician if the blindtest sample matches the expected input drug, step 236 of FIG. 23. Inidentification, the y_(m) ^(B) values for the unknown sample areanalysed against the y_(m) values for all training samples. Theprocessor 18 can provide an output on the user interface for exampleadvising the clinician what the sample drug is, step 236 of FIG. 23 andalso control external equipment where appropriate.

One possible embodiment of a verification/identification method isdescribed with reference to FIG. 25 (which shows step 235 e of FIG. 24in more detail)—the processor 18 undertakes the steps. As previouslydescribed, each unknown sample and training sample have a set ofcoefficients, one for each wavelength. In overview, a linear score isdefined for the set of coefficients for each sample by 6 weights—one foreach wavelength: score=w₁×normalised value at wavelength 1+ . . .+w₆×normalised value at wavelength 6. In addition, for each score athreshold value is determined, τ, such that an alarm is raised when thescore e.g. exceeds τ.

First a sample coefficient is obtained by the processor 18 from thedatabase 23, step 240. It is then multiplied by or otherwise has aweighting applied to it, step 241. The weighting is added to a previousweighting for that sample, step 242. This provides a cumulativeweighting which becomes a representative sample value for the sample. Ifall coefficients for the sample have been processed, step 243, themethod moves to the next step. If not, step 243, the next coefficient isobtained, step 240, weighted, step 241, and added to the cumulativeweighting, step 242 for that sample.

Next, the same process happens for the training sample coefficients—theprocessor 18 undertakes the steps. If verification takes place thenfollowing happens. The first coefficient is obtained by the processor 18for the sample/drug that the clinician input previously as the predicteddrug, step 244. It is then multiplied by or otherwise has a weightingapplied to it, step 245. The weighting is added to a previous weightingfor that training sample, step 246. This provides a cumulative weightingwhich becomes a representative training value for the training sample.If all coefficients for the sample have been processed, step 247, themethod moves to the next step. If not, step 247, the next coefficient isobtained, step 244, for that training sample weighted, step 245, andadded to the cumulative weighting, step 246 for that sample.

Next the cumulative representative training value and the cumulativerepresentative sample value are compared or compared against athreshold(s) or some other relationship between them is determined, step248. For example, if the sample value is within ‘X’ of the trainingvalue or if the sample value is above or below a threshold with somereference to the training value, then a “match” is determined, and theunknown sample is deemed the same as that of the training value.Otherwise it is deemed not to be a match. Output as previously describedcan then take place indicating the result, step 236. The process stops.

Where identification takes placed, steps 244 to 248 cycles through forall training sample coefficients for all training samples, step 249.That is, if the comparison step 248 results in no match, step 249, thenthe processor 18 determines the representative training sample valuefrom the training coefficients for the next training sample in thedatabase 23, steps 244 to 247. The training and sample representativevalues are compared, and it is determined whether a match occurs, step248, and the result outputted step 236. If there is no match, step 249,steps 244 to 248 are repeated until all training sample coefficientshave been analysed, or a match occurs.

It will be appreciated that the above embodiment is conceptual only andthe actual steps taken and their order by a processor could bedifferent. For example, training samples coefficients could be processedfirst. Many alternatives could be envisaged.

Determining Weights and Thresholds

The drugs below were tested using and apparatus and method as describedin the first embodiment. The weights w₁ . . . w₆ are chosen by solving alinear program that provides a separation of 1 unit in the score. Thisis possible if the ‘intended’ drug is well-separated from the rest.However, even when it is possible to get a solution, there is the issueof ‘robustness’. Large weights are symptomatic of a lack of‘robustness’. To get a better idea of blind test performance we willneed to add +/−1% to the training data and consider the resulting falseand missed alarm rates. The threshold value T is chosen to giveacceptable error rates (if possible).

Determination of Drug Concentration.

With the drug identity verified, the slope of the concentration curveS_(m) can now be found by the processor using equation (8). Then, theconcentration of the drug in its chosen dilutant can be found by theprocessor from back-substitution into equation (10) on setting S_(m)^(B)=S_(m). Thus, the concentration, x, is given by:

$\begin{matrix}{x = \frac{{g_{m}^{B}(x)} - \overset{\_}{g_{m}^{B}}}{s_{m}}} & (12)\end{matrix}$

The processor can provide output on the user interface advising theclinician what the concentration of the sample drug is.

It will be appreciated that in this embodiment that not all correctionsor processing are essential. While the raw data is described as havingdark current eliminated, temperature corrected, fractional intensitiesfound, and concentration-independent coefficients found, a subset ofthese could be used. Further, the order in which they are described asoccurring should not be considered limiting. It will also be appreciatedthat while temperature correction, fractional intensity andconcentration independent coefficients are found after the blind test,this is not essential. Some or all of these might be found after thetraining test. A database 23 (e.g. in the form of a look up table) couldbe produced at training acquisition time or afterwards and then used bythe characterisation process after the blind test. Training samplecoefficients could be provided for e.g. all likely temperatures and thenused by the processor 18 during characterisation.

It will also be appreciated that where verification takes place, it maynot be necessary to process and compare all training data/coefficients.Rather, just the training data/coefficients for the identified drug areprocessed and compared to those of the unknown sample. In identificationthe training data/coefficients for several or all of the trainingsamples may need processing/comparing with the unknown drug until amatch is found.

Second Embodiment

One possible embodiment of the invention will now be described in detailby way of example. This should not be considered limiting butillustrative. The embodiment is described in relation to an apparatusfor providing verification or identification of water based drugs frome.g. a set of 30 drugs.

Six wavelengths of electromagnetic radiation are chosen for thisexample, six being greater than log₂n of 30. The wavelengths are chosenin the analysis range and are based on the spectral characteristics ofwater, being the base liquid, falling in that range. The spectrum of awater based drug (or other liquid based drug or aqueous solution) willbe heavily dominated by the base liquid spectrum. For example referringto FIG. 5, the spectrum (dotted line) of drug W (gelofusine succinatedgelatine solution 4%) is very similar to the spectrum of water (solidline). This is because the spectrum of water dominates. However, thedifferences in transmission coefficient between different water baseddrugs can be measured. Focussing on areas/wavelengths of spectralcharacteristics of the water spectrum, by using electromagneticradiation beams at those wavelengths, the difference between the waterspectrum and the water based drug spectrum at those wavelengths can beutilised to provide drug discrimination for drug identification orverification.

FIG. 6 shows a spectrum of water with some possible spectralcharacteristics (features) in the analysis range identified, andexplained further below.

-   -   Spectral characteristic A (slope)—in a first region between 1300        nm and 1400 nm.    -   Spectral characteristic B (plateau/trough)—in a second region        between 1400 nm and 1500 nm.    -   Spectral characteristic C (slope)—in a third region between 1500        nm and 1600 nm.    -   Spectral characteristic D (peak)—in a fourth region between 1600        nm and 1700 nm.    -   Spectral characteristic E (inflection)—in a fifth region between        1700 nm and 1800 nm.    -   Spectral characteristic F (knee) a sixth region between 1800 nm        and 2000 nm.

This is not an exhaustive list of possible spectral features.

The selection of a wavelength for an electromagnetic radiation beam isnot strictly fixed, and not necessarily solely based on spectralcharacteristics of the base liquid. It is influenced by the wavelengthof spectral characteristics in spectrum of the base water of the drugsample, but in addition the selected wavelength can be based on otherfactors also. For example, in interest of cost effectiveness and aregularly obtainable supply chain, it might be preferable to use orselect an alternative wavelength that is close to the spectralcharacteristic but not quite the same, if that alternative wavelength iseasily obtainable by an off-the-shelf laser or other optical component.

For example, it is possible to use 1310 and 1550 nm as selectedwavelengths for water based drugs as there are many devices configuredfor these wavelengths as they have wide spread use within thecommunications industry. Laser diodes nominally have centred wavelengthsat 1650 nanometers, 1750 nanometers and 1850 nanometers, although thesecan be varied by up to plus or minus 30 nanometers. So wavelengths inthese ranges can also be selected. Therefore by looking at theavailability of these components, and the spectral characteristics ofthe base liquid, suitable wavelengths for the emitted radiation can bedetermined.

Therefore, based on the above explanation, each of the six wavelengthscan be chosen to be within the vicinity or within the region spanningone of each of the spectral features, but also influenced by theavailability of hardware. The six wavelengths for water could thereforebe (by way of example): 1350 nanometers corresponding to feature A, 1450nanometers corresponding to feature B, 1550 nanometers corresponding tofeature C, 1650 nanometers corresponding to feature D, 1750 nanometerscorresponding to feature E and 1850 nanometers corresponding to featureF, all which fall within the 1300-2000 nanometers. As can be seen the1350 nm to 1850 nm wavelength selections do not match exactly to peaksand troughs and other spectral characteristics in the water spectrum,although are close. The selections also relate to operating wavelengthsof available hardware. These are of course nominal wavelengths and theactual wavelength might vary in practice due to source 11characteristics.

FIG. 7 shows in schematic form one possible form of the apparatus 10 asgenerally described in FIG. 1. The spectroscopic analyser 10 has acontroller 12 and a carousel 50 that supports six lasers 51 a-51 f,which together form the source 11 to output electromagnetic radiation 22at a plurality of wavelengths in the form of light. Each laser is tunedor tuneable to emit electromagnetic radiation 22 at one of the sixwavelengths defined above. Each laser can comprise or be formed fromlaser diodes providing a stable, high intensity, narrow band collimatedelectromagnetic radiation output that is readily controlledelectronically via driver circuitry. Each laser comprises a lens thatcan collimate the emitted electromagnetic radiation 14 a into a beamusing appropriate lenses. Each laser 51 a-51 f can have one or morephotodiodes 4 a-4 f for detecting output electromagnetic radiation forfeedback control of that radiation. Lasers have fewer heat emissionproblems than other sources, thus reducing the detrimental effects ofheat on the measurements. The output power of each laser preferably isnominally the same (typically 30 mW) in the interests of having abalanced apparatus. Preferably, this also enables a common diode drivercircuit to be used for the laser diodes.

The controller 12 can control the carousel 50 to rotate about an axis toactivate any one of the lasers 51 a-51 f in turn and align the activatedlaser (e.g. 51 f as shown) to emit a beam 22 along the sample path/beampath 14 a. The lasers 51 a-51 f can also be turned off completely tofacilitate the measurement of dark current signals if required. The useof mechanically activated optical chopper can thereby be eliminated(although one can be included if desired.) Once activated, the laseremits electromagnetic radiation 22 towards the sample along the path 14a. The path 14 a from the source to the detector is preferablypredominantly via free-space preferably with minimal if any opticalfibre components. This reduces optical attenuation and hardware. Theapparatus also comprises a sample retainer 16 a, which is aligned withthe beam path 14 a. The emitted electromagnetic radiation from an activelaser 51 a-51 f is incident on and transmits or reflects through thesample 16 in the sample retainer.

The detector 16 is placed in the affected radiation path 14 b that exitsthe sample 16 a. Preferably the detector 16 is a singlephotodetector/photodiode biased to have a suitable response to detectelectromagnetic radiation of wavelengths that will be in the affectedradiation. A single detector reduces the errors due to variabilityintroduced by components—it removes the relative differences betweenmultiple photodetectors enabling a more stable response to the output ofthe emitted electromagnetic radiation thus enhancing sensitivity. AnInGaAs photodiode could be used, for example. The detector 17 detectsthe affected radiation 14 b and the output 14 c of the detector 17 ispassed to a processor 18 that verifies or identifies the sample asdescribed above.

The apparatus also has a beam splitter 21 to redirect the incidentelectromagnetic radiation beam 22/14 a towards a reference sampleretainer along a reference path 15 a, which passes through to areference detector 20. The output of the reference detector 20 is alsopassed to the processor 18. The reference could be saline, for example.

Preferably, the apparatus also comprises a feedback system to stabilisethe temperature of the electromagnetic radiation source 11 and thedetectors(s). In one example, thermistors detect the temperature of theelectromagnetic radiation source and/or detector(s). Peltier coolingdevices can be operated to cool and stabilise the temperate of thesource and detectors. The output of the thermistor(s) is sent to thecontroller, which controls the peltier cooling devices to cool thesource and/or detectors. Preferably the thermistor is the built-inphotodetector thermistor 5 a, 5 b. And the peltier thermo-electriccooler is built-in to the photodetector 5 a, 5 b.

Referring to FIG. 4, operation of the apparatus 10 will now bedescribed. The controller 12 operates the carousel 50 to rotate eachlaser 51 a-51 f in turn to the activate position. When in the activateposition, the laser 51 a-51 f is operated by the controller 12 to emitan electromagnetic radiation beam at one of the selected wavelengths tothe sample 16 (and optionally to reference sample 19.) In this manner,six electromagnetic radiation beams with different selected wavelengthsare emitted, step 40, in sequence from each of the six lasers 51 a-51 f,each tuned to a different selected wavelength. Each laser 51 a-51 f inturn emits an electromagnetic beam 22 along the path 14 a towards thesample. The affected radiation coming from the sample is detected, step41, for each electromagnetic radiation beam emitted 14 a towards thesample 16. The electromagnetic radiation beam could be switched on andoff to get a reading/measurement made by the detector during the offphase also—this can give a dark signal/current for reference purposes.The emitted electromagnetic radiation is also directed along thereference path 15 a, through the reference sample 19 using the beamsplitter 21, and detected by the reference detector 20. The outputs fromthe sample detector 17 and the reference detector 20 are passed to theprocessor 18, step 42. The processor (optionally) carries outpre-processing on the output from the detectors, and then verifies oridentifies the drug based on the pre-processed outputs, step 43. Itoutputs the results via the user interface 24, step 44.

In one possible embodiment, the processor 18 comprises or implements apre-processing method and then a verification/identification method asshown in FIG. 8. In this embodiment a reference channel is used and alsodark current readings. Dark current is the output provided by thedetectors 17, 20 when no electromagnetic radiation (e.g. light) isincident on them. This dark current reading from the detector can besubtracted from the actual reading from the detectors for calibrationpurposes. Having a dark reading is not essential for the invention andis described here as one possible option—the remaining description ofthe processing method would work also without dark readings being takenor by.

Prior to carrying out the verification or identification in FIG. 8, atraining process is carried out to produce a comparison data from whichsamples can be verified/identified as shown in FIGS. 9-11. In thetraining process, an algorithm is used to generate the comparison data,which determines the particular linear combination of data values fromeach of the sample data that optimises the separation between differentdrugs. The resulting mathematical rule is then applied to the dataacquired for the drug under test to verify that it is the intended drug.In the embodiment described, dark current readings are used. Thetraining process preferably comprises a pre-processing stage, and acomparison data generation stage. Pre-processing is not essential, butimproves performance.

Referring to FIG. 9, for the training process, a number of trainingsamples are tested in the analyser in turn. Each training sample relatesto a sample that will be test for during actual use of the analyser. Foreach training sample, output from both sample and reference channels isreceived at the processor, step 90. If dark current is being used, theoutput from each detector for the dark reading is subtracted from theoutput of the actual reading. The output 14 c received at the processor18 from the sample detector 17 indicates the intensity of the affectedelectromagnetic radiation 14 b for each emitted electromagneticradiation beam at the sample 16. It may, for example, comprise datawhich directly or indirectly indicates photocurrent of the detectorand/or intensity of the detected electromagnetic radiation. Likewise,the output 15 c received at the processor 18 from the reference detector20 indicates the intensity of the affected electromagnetic radiation 15b for each emitted electromagnetic radiation beam at the referencesample. Preferably, the apparatus carries out multiple measurements foreach wavelength. For example, at each wavelength, the apparatus detectsaffected electromagnetic radiation affected by the sample at 15different times and passes this output to the processor, step 94.Similarly, at each wavelength, the apparatus detects affectedelectromagnetic radiation affected by the reference at 15 differenttimes and passes this output to the processor, step 94.

Next, for each wavelength, the processor 18 generates from the output ofthe reference and sample detectors a range of sample data points for thesample that correlate an intensity of affected electromagnetic radiation14 b affected by the sample at a particular selected wavelength, step91. These data points 100 could be plotted, as shown for example in FIG.10—although it will be appreciated that the processor does notnecessarily actually plot the data. The x axis shows intensityindicative values corresponding to detector output for the sampledetector 17, and the y axis shows intensity indicative valuescorrelating to detector output for the reference detector 20. The valuesindicate directly or indirectly the intensity of detected affectedelectromagnetic radiation. Where a reference channel is used, output onthe reference detector is paired with output from the sample detectortaken at the same time. Each sample/reference channel detector outputvalue pair is plotted on the graph. Such measurements can be taken forseveral times for each wavelength. Therefore, the plot in FIG. 10 showsthe values indicative of intensity 103 measured at several times (e.g.15) for a particular selected wavelength (e.g. nominally 1350 nm) ofelectromagnetic radiation incident 14 a on the training sa 16 and on thereference 19.

For each training sample, the process is then repeated to get similardata points for a second (comparison) sample 101 and a control (e.g.saline) 102. The sample/reference channel detector output value pairsfor the second (comparison) 101 sample and control sample 102 could alsoplotted on the graph, as shown in FIG. 9, step 91.

A best fit straight line can then be calculated using a suitablestatistical technique, step 92, and the intercept value of the x axis isfound, step 92, for each of the:

-   -   training sample set, 103    -   second (comparison) sample 101, and the    -   control sample 102        set of data points for the particular wavelength (1350 nm), as        shown in FIG. 10.

From this a normalised pre-processed value is found. For example, thex-axis intercept values (e.g. 842500 and 850500) for the training sample103 and control 102 respectively can be found, and then can besubtracted from each other to obtain normalised pre-processed values(e.g. 8000), step 93. Similarly, the x-axis intercept values (e.g. 86000and 850500) for the second (comparison) sample 101 and control 102respectively can be found also, and then subtracted from each other toobtain normalised pre-processed values (e.g. 95000), step 93. Thisprocess can be carried out for each of the other selected wavelengths(e.g. five others in this case), step 94 and steps 90-93, resulting in aset of six normalised pre-processed values (−one for each wavelength)for the training sample. The process can also be carried out for each ofthe other selected wavelengths for the second (comparison) sample,resulting in a set of six normalised pre-processed values for the second(comparison) drug for each wavelength. These sets of normalisedpre-processed values for the training sample and second (comparison) foreach wavelength sample can be correlated/plotted in a multidimensionalspace, each axis corresponding to a wavelength and the pre-processedvalue for that wavelength being plotted relative to that axis.

In practice, this process, steps 90-94, can then be carried out numeroustimes for each wavelength, so that for each training sample and second(comparison) sample, there are a plurality of sets of six normalisedpre-processed values. Each set can be plotted/correlated as one point ina multidimensional (six dimensions in this case) space. An example ofsuch a plot is shown in FIG. 11. Here, for simplicity, only a twodimensional space is shown, each axis relating to the results from twowavelengths—in reality it would need to be a six-dimensional graph tocover all six wavelengths. For each set for each of the training sampleand second (comparison) sample, a pair of two normalised pre-processedvalue (i.e. one value for each wavelength) is plotted as a single pointon the two dimensional graph, e.g. 110, resulting in a normalisedpre-processed value data set for the training sample 111 and the second(comparison) sample 112.

The pre-processing stage described above reduces the detrimental effectsof systematic errors in the system and drift in the measured data. Note,the reference channel/value is optional. In an alternative, x-axisintercept values are found for the sample data only.

In an alternative embodiment, the pre-processing steps previouslydescribed can be omitted on the grounds that system drift and systematicerrors can be virtually eliminated with the use of highly stable laserdiode sources and a reference signal derived from the laser's ownmonitor diode output. This facilitates the use of a single channel witha single photo-detector eliminating the need for separate opticalreference channel and/or control sample to be used. To this end, thedata base of measured transmission spectra for a range of intravenousdrugs can be built up in a more straightforward manner by sequentiallymeasuring samples of each drug in a single channel using multiple testtubes.

After the data has been pre-processed for the training sample and second(comparison) sample and correlated as shown in FIG. 11, a representativevalue can be obtained for the training sample. If no pre-processing iscarried out, the process proceeds to finding the representative value onnon-pre-processed (raw) data. First a line 113 that separates thetraining sample data set 111 from the second (comparison) sample dataset 112 is determined, step 95. Then the normal direction of the line isused as a weighting in a score to separate the training sample from thecomparison sample. Also, a threshold is determined below which thetraining sample falls, step 96. The threshold and weighting scoreprovide a representative value for comparison data to assist inverification/identification for that training sample. The representativevalue is stored as comparison data in a database 23 for the trainingsample, step 98.

The entire process is the repeated (step 99, and steps 90-98) for thesame training sample against a third (comparison) sample to get a secondrepresentative value for storing as comparison data in the database 23for the training sample. Then the process is repeated again (step 99,and steps 90-98) against a fourth and subsequent comparison samples togenerated a third and subsequent representative values for storing ascomparison data for the training sample. Together these form therepresentative values in the comparison database to identify/verify thetraining sample.

The entire process (step 100, step 90-99) is the repeated for each othertraining sample (in the set of n drugs) against multiple comparisonsamples, in order to obtain representative values for each additionaltraining sample also.

It will be appreciated that in describing the training process steps90-100, there has been reference to graphs and techniques. These aredescribed for illustrative purposes. Any processor carrying out thetraining process to determine representative values might not actuallyproduce such graphs or utilise such techniques to obtain the end result,but rather use other processing techniques that achieve the same result.

The above training process will generate comparison data for eachtraining sample (in the set of n drugs) that can stored in the database23 and can be used to identify or verify actual samples from the setunder test. The comparison database 23 can be generated well in advanceof actual sample testing, or can be generated soon before or evenon-the-fly. The comparison data can be considered as a multidimensionalverification/identification matrix based on the acquiredmultidimensional spectral data from the detectors. The comparison datacan be used to verify or identify any of the drugs from any of the otherdrugs in the set of n drugs.

Referring back to FIG. 8, once a comparison database is produced andstored in the database 23, verification/identification of actual samplesoccurs as follows. Output from both sample and reference channels isreceived at the processor, step 80. If dark current is being used, theoutput from each detector for the dark reading is subtracted from theoutput of the actual reading. The output 14 c received at the processor18 from the sample detector 17 indicates the intensity of the affectedelectromagnetic radiation 14 b for each emitted electromagneticradiation beam at the sample 16. It may, for example, comprise datawhich directly or indirectly indicates photocurrent of the detectorand/or intensity of the detected electromagnetic radiation. Likewise,the output 15 c received at the processor 18 from the reference detector20 indicates the intensity of the affected electromagnetic radiation 15b for each emitted electromagnetic radiation beam at the referencesample. Preferably, the apparatus carries out multiple measurements foreach wavelength. For example, at each wavelength, the apparatus detectsaffected electromagnetic radiation affected by the sample at 15different times and passes this output to the processor, step 80.Similarly, at each wavelength, the apparatus detects affectedelectromagnetic radiation affected by the reference at 15 differenttimes and passes this output to the processor, step 80.

This output is then preferably pre-processed, steps 81-84, in the samemanner as described above for the training process and with reference toFIGS. 9 to 11. That description need not be repeated here, but insummary, data points are generated, step 81, best fit lines found, step82, and x-axis values are obtained which provide normalisedpre-processed values, step 83. This is done for all wavelengths, step84. Pre-processing is not essential, but can improve performance.

After this pre-processing is carried out for the affected radiation ofeach wavelength, steps 81-84, the identification/verification algorithmcan then be invoked, step 85. Verification involves confirming that asample drug is the drug that is expected. For example, a clinician canspecify what they think the drug is (e.g. from the set of n drugs)through the user interface 24, e.g. step 80, then use the apparatus toconfirm whether the drug in the retainer is actually that drug which isspecified by the clinician. Identification involves determining what adrug actually is, without any suggestion from the clinician as to whatthe drug is. For verification/identification, the spectral data (thatis, the pre-processed values) are compared against the comparison datain the database 23, step 85, to identify the drug, or verify whether itis the anticipated drug as specified by the clinician. Output is thenprovided to the user interface, step 86.

In one possible identification/verification algorithm, once the sampledata is obtained and pre-processed, representative values are found forthe sample, in the same manner that they were found during the trainingprocess as explained with reference to FIGS. 9 to 11. The representativevalues are found for the sample at each selected wavelength and withrespect to each other comparison sample. The representative values arecompared to the representative values in the comparison data. If thereis sufficient similarity between the representative values found for thesample and the representative values in the comparison datacorresponding to the same sample, then verification or identification ismade. Sufficient similarity can be determined using any suitablestatistical or other technique. For example, sufficient similarity mightoccur when some or all of the representative values match those in theverification matrix. In another example, this might occur when thesample falls below the threshold for each comparison sample. An alarm oroutput might be made via a user interface to advise the user of theresult of the verification/identification.

FIG. 15 shows test data for a set of 30 drugs verified using theanalyser. In the test, each drug was inserted in the analyser, and thensystematically the analyser was configured to check if it was one of the30 drugs. If an alarm was raised, this indicated the drug was not theone that was anticipated, and the alarm noted. Each drug was tested 15times, in relation to each of the other drugs. So, for example,Metaraminol was put into the analyser and then the analyser wasconfigured to check for Metaraminol. After 15 tests, the analyser didnot once raise an alarm, indicating that the analyser did not detectMetaraminol as another drug. Keeping Metaraminol in the sample retainer,the analyser was then configured to check for Heparin. For each of 15independent tests, the analyser raised an alarm, indicating it detectedeach time that the drug in the analyser (Metaraminol) was not the drugit was expecting (Heparin). The analyser was then reconfigured for eachof the other drugs, and the test done 15 times for each, whileMetaraminol was in the sample retainer. The same process was thenrepeated for every other drug being used as a sample, with the analysersystematically being re-configured to check for every other drug. Eachtime an alarm was raised (indicating the analyser did not consider thedrug in the retainer was that being checked form), the alarm was noted.The table in FIG. 15 reflects the number of times an alarm was raised ofeach drug detection combination. The error rates are shown. The lowerror rates demonstrate a significant improvement in verificationaccuracy.

Third Embodiment

FIG. 12 shows an alternative embodiment of the apparatus 10. In thisembodiment rather than using a carousel 50, the six lasers 51 a-51 fforming the source 11 are arranged to emit their electromagneticradiation beam 22 towards a diffraction grating 120 of the reflectiontype. Each laser 51 a-51 f is operable to emit a tuned or tuneablewavelength of a collimated electromagnetic beam 22 towards thediffraction grating. The angle of incidence X on the grating surface foreach laser 51 a-51 f is chosen that their first order diffracted beamemerges at the same angle Y thereby producing a common optical path 14 afor each laser. The controller 12 activates each laser 51 a-51 fsequentially to emit a beam of a single wavelength towards the sample.Alternatively, multiple lasers 51 a-51 f could be operated at once suchthat an electromagnetic beam 22 comprising multiple wavelengthcomponents could be emitted towards the sample 16. A separate grating orbeam splitter 21 could be used for example as shown in FIG. 1 to directthe beam towards a reference channel sample 19, if there is one. Allother aspects of the embodiment can be as shown and described in FIGS.1, 2, 16 and/or 18.

Fourth Embodiment

FIG. 13 shows another alternative embodiment of the apparatus 10. Inthis embodiment rather than using a carousel 50, the six lasers 51 a-51f forming the source 11 are arranged to emit their electromagneticradiation beam 14 a towards respective beam splitters 130 a-130 f thatredirect the emitted electromagnetic radiation beam 22 along the samplepath 14 a. The controller 12 can control each electromagnetic radiationsource 11 in turn to emit a tune or tuneable wavelength ofelectromagnetic radiation towards the sample via the respective beamsplitter 130 a-130 f. Alternatively, two or more of the lasers 51 a-51 fcould be activated at once to provide an electromagnetic beam 22 withmultiple wavelength components towards 14 a the sample 16. An absorber135 is provided behind the beam splitter array to mop up transmittedenergy from the beam splitters. A separate grating or beam splitter 21could be used for example as shown in FIG. 1 to direct the beam towardsa reference channel sample 19, if there is one. All other aspects of theembodiment can be as shown and described in FIGS. 1, 2, 16 and/or 18.

Fifth Embodiment

FIG. 14 shows an alternative embodiment of the apparatus 10. In thisembodiment rather than using a carousel 50, the six lasers 51 a-51 fforming the source 11 are arranged to emit their electromagneticradiation beam 22 towards a prism 140. Each laser 51 a-51 f is operableto emit a tuned or tuneable wavelength of a collimated electromagneticbeam 14 a towards the prism. The angle of incidence X on the gratingsurface for each laser 51 a-51 f is chosen that their first orderrefracted beam 22 emerges 14 a at the same angle Y thereby producing acommon optical path 14 a for each laser 51 a-51 f. The controller 12activates each laser 51 a-51 f sequentially to emit a beam of a singlewavelength towards the sample. Alternatively, multiple lasers 51 a-51 fcould be operated at once such that an electromagnetic beam 22comprising multiple wavelength components could be emitted towards 14 athe sample 16. A separate grating or beam splitter 21 could be used forexample as shown in FIG. 1 to direct the beam towards a referencechannel sample 19, if there is one. All other aspects of the embodimentcan be as shown and described in FIGS. 1, 2, 16 and/or 18.

Sixth Embodiment

FIG. 20 shows an alternative embodiment of the apparatus 10. In thisembodiment rather than using a carousel 50, the six lasers 51 a-51 fforming the source 11 are arranged to emit their electromagneticradiation beam 22 through separate fibre optic cables 201 a-201 ftowards a planar lightwave circuit (PLC) (fibre optic combiner) 200.Each laser 51 a-51 f is operable to emit a tuned or tuneable wavelengthof a collimated electromagnetic beam 14 a towards the PLC 200 via thefibre optic cables 201 a-201 f. The controller 12 activates each laser51 a-51 f sequentially to emit a beam of a single wavelength towards thesample. Alternatively, multiple lasers 51 a-51 f could be operated atonce such that an electromagnetic beam 22 comprising multiple wavelengthcomponents could be emitted towards 14 a the sample 16. A separategrating or beam splitter 21 could be used for example as shown in FIG. 1to direct the beam towards a reference channel sample 19, if there isone. All other aspects of the embodiment can be as shown and describedin FIGS. 1, 2, 16 and/or 18.

Seventh Embodiment

FIG. 21 shows an alternative embodiment of the apparatus 10. In thisembodiment rather than using a carousel 50, a single package 211comprising 6 lasers forming the source 11 are arranged to emit theirelectromagnetic radiation beam 201 a-201 f towards an integratedcollimating lens 210. The laser is operable to emit a tuned or tuneablewavelength at each of 6 wavelengths towards the lens 210. The controller12 activates the laser to sequentially to emit a beam 212 a-212 f of asingle wavelength towards the sample. Alternatively, multiple beams 51a-51 f could be operated at once such that an electromagnetic beam 22comprising multiple wavelength components could be emitted towards 14 athe sample 16 via the lens 210. A separate grating or beam splitter 21could be used for example as shown in FIG. 1 to direct the beam towardsa reference channel sample 19, if there is one. All other aspects of theembodiment can be as shown and described in FIGS. 1, 2, 16 and/or 18.

Alternative Embodiments

The nominal analysis range of 1300-2000 nm for selected wavelengths ischosen as it provides advantages for improved drug verification oridentification. However, it will be appreciated that the reference to1300-2000 nm should not be considered limiting, and wavelengths could bechosen that relate spectral characteristics in slightly different rangesor other ranges entirely. The selected wavelengths (and therefore thespectral characteristics) fall within any analysis range provide forimproved identification/verification for drugs in the liquid carrier.For example, the analysis range could be a subset of 1300 nm-2000 nm,such as 1300 nm-1900 nm; 1350 nm-1950 nm; 1400 nm-1900 nm; 1500 nm-1800nm or some other subset. The range could also be larger, such as1250-2050 nm; 1200 nm-2100 nm; or 1150 nm-2150 nm or the like. Theanalysis range might even be offset from the nominal range, such as 1200nm-1900 nm, or 1300 nm-1900 nm. These are non-limiting examples. Ingeneral, the analysis range could start, for example, anywhere from 1100nm-1500 nm and end anywhere from 1800 nm-2150 nm. Even that isnon-limiting and the range could be something different entirely thatprovides for improved verification/identification. Further, wavelengthsfalling outside these analysis ranges and corresponding to spectralfeatures lying outside these analysis ranges above could also be used incombination with wavelengths falling in the analysis ranges mentioned.Using a plurality of wavelengths corresponding to spectralcharacteristics falling within the analysis range provides improvedperformance. Preferably any and all wavelengths are selected within theanalysis range, but that does not preclude using wavelengths falling inother ranges also where that might be useful.

The range could be at least partially influenced by component selection.For example, silicon photodiodes have a response down to at least 1100nm, so if used this wavelength might be used as the bottom end of therange. Preferably, the invention uses only one detector, so the rangemight be defined by what a single detector can cover—for example 1300nm-2000 nm in the case of an InGaAs detector.

Other liquids to water might have other analysis ranges that provideimproved identification/verification.

Other methods for extracting the information could be known and used bythe skilled in the art.

In an alternative analysis process, a reference channel is not used.Rather, the detector output 14 c from affected electromagnetic radiation(from the sample) acquired at an anchor wavelength is used, rather thanthe detector output 15 c from affected electromagnetic radiation fromthe reference in the reference channel. All other detector output 14 cfrom affected electromagnetic radiation received relating to otherwavelengths is normalised/corrected using the detector output ofaffected electromagnetic radiation at the anchor wavelength. The anchorwavelength can be one of the wavelengths already selected, althoughpreferably will be selected to be in the vicinity or within a regionspanning a suitable spectral feature/point in the base liquid spectrum.For example, the anchor wavelength could be in the vicinity of or fallwithin a region spanning a stable region of the base liquid spectrum.Elimination of the reference channel/detector output removes variationbetween the sample and reference channels that can mask sampledifferences, thus removal creates a more sensitive and stable apparatus.The output at the anchor wavelength can be used to normalise, calibrateor otherwise adjust the output for the other wavelengths. The outputfrom the anchor wavelength could be processed in the same manner as theoutput from the reference channel as describe previously in order toverify/analyse the sample. That is, the anchor output can become thereference information.

In one possibility, where water is the base liquid, 1450 nanometers ischosen as the anchor point as there is particular stability in thespectrum of water around this wavelength. This wavelength corresponds tothe maximum optical absorption aqueous solutions due to the presence ofOH bonds. It is a common transmission medium for sample drugs tested.Data acquired at this wavelength shows minimum thermal sensitivity andis therefore provides a highly stable and predictable reference. This isjust one example for water based drug, and is indicative only and shouldnot be considered limiting as to the wavelengths and anchor points thatmight be chosen based on other considerations.

Each of the previous embodiments describe the optional use of areference channel to obtain reference measurements for use in processingdata. In an alternative, the reference channel is not used. Rather, aphotodiode 4 (see FIG. 20) in the laser diode 11 (which is used forpower monitoring and control of the laser diode) can be utilised toobtain reference information. Laser diodes are often fitted withbuilt-in photo-detector diodes 4 that are used to monitor the outputpower of the laser. This is done to stabilise the laser by allowing thelaser driver current to be controlled via a feedback circuitincorporating the integrated photo-diode signal.

This alternative for obtaining reference information can be substitutedin place of the reference channel for any of the embodiments described.The reference measurements obtained using the alternative can beutilised in the same manner as described any previous embodiment.

The output of the laser diode photodetector 4 which detects the outputpower of the source electromagnetic radiation is passed to the processor18 and used instead of reference readings obtained by the referencedetector 20 to normalise and/or correct the output from the detector 17in the sample channel. This output signal from the photodetector 4performs the same function as a reference channel that would otherwisehave been produced more conventionally by using a beam splitterarrangement involving two separate measurement channels. Using thephoto-diode output from the laser as a reference signal therebyeliminates the need for beam-splitting optics and an additionalreference sample and detector.

In an alternative embodiment, the electromagnetic source 11 is abroadband source with multiple filters 13 at different wavelengths thatcan be arranged in between the broadband source and the sample. Theoutput from each filter provides an electromagnetic beam 22 with one ofthe selected wavelengths. The broadband source could be, for example, abroadband filament blackbody source and filters. The source 11 couldalternatively take the form of one or more LEDs with or without filters.Any of the alternative sources could be mounted on a carousel 50 andoperated as described for the first embodiment, or operated inconjunction with an optical device such as described in embodiments twoto four.

Any of the sources could be temperature stabilised with a feedbacksystem, for example by using thermistors and peltier cooling devices aspreviously described.

The detectors could be in the form of one or more InGaAs photodiodes orother light sensors.

A separate photodiode or similar or other detector could be used foreach of the reference and sample channels. Alternatively, a singlephotodiode or similar or other detector could be used for both thesample and reference channels, utilising optical devices to merge theaffected radiation beams of both channels, or otherwise direct them tothe detector.

Random errors in measurements can be reduced by averaging detectorreadings over many measurements (e.g. 500). Dark measurements (sourceoff) can be used to correct measured data.

For dark current readings, a chopper wheel can optionally be used thatblacks out/blocks the electromagnetic radiation 22 incident on thesample 16 and the reference 20. The chopper could form part of theoptical device 13. For each electromagnetic reading, the detector 17/20also takes a “dark” reading when the chopper blocks the electromagneticradiation 22. Having a chopper wheel and dark reading is not essentialfor the invention and is described here as one possible option.

Over the band 1300 nm to 2000 nm, it is also possible to use a singletype of photo-diode detector based on indium gallium arsenide (InGaAs)technology which further simplifies the detector system.

The present invention preferably uses wavelengths in the analysis regionof 1300 nm to 2000 nm or variations thereof. This region has previouslybeen ignored for drug analysis due to the perceived disadvantage ofbroad spectral peaks and troughs that appear in the absorbance spectrum.Infra-red (IR) spectroscopy previously has exploited the numerousnarrow-band spectral absorption characteristics that exist forwavelengths longer than 2000 nm. This so-called ‘finger-print’ regionexhibits spectral lines that are characteristic of certain chemicalbonds present in the material under test and offers a highly sensitivetechnique to identifying the material. The present inventors havedetermined that the 1300 nm-2000 nm analysis range (or portions thereof)provides an advantage for drug verification or identification or otheranalysis. Further, the inventors have established that the spectrallocation of salient spectral features in this analysis region is lessaffected by temperature variations. The numerous narrow spectral bandsthat appear in the region above 2000 nm exhibit large temperaturesensitivity. If this region above 2000 nm is used for verification oridentification, the analysis apparatus requires very precise wavelengthresolution. This resolution can only be achieved using high-costsophisticated spectrometers

More particularly, this type of IR spectroscopic measurement (above 2000nm) requires very fine wavelength resolution (typically a fewnanometers) maintained over a wide spectral band in order to resolve thenumerous individual spectral features. The fine wavelength resolution isespecially required to account for any shift in the narrow spectrallines with respect to temperature variations.

The measurement of such highly resolved spectral lines requires the useof a spectrometer fitted with a sophisticated monochromator based eitheron a mechanically rotated diffraction grating and single detector, or afixed grating with a linear array of detector elements. Both options arefound in existing spectrometers and both are expensive to implement.

As a cost-effective alternative, aimed for example at water-basedintravenous drug verification/identification or other analysis, it hasbeen determined by the present inventors that it is advantageous to makemeasurements within the shorter wavelength region between 1300 nm and2000 nm. Whilst the spectral characteristics/features in this wavelengthregion are much fewer in number and much broader spectrally (differinglittle from those of water), the inventors have found that there remainsufficient spectral differences between drugs (or other liquid basedsamples) to facilitate verification/identification. The have also found,that, in the 1300 nm to 2000 nm region, the wavelengths at which thepeaks and troughs (and other spectral characteristics) of each drug's IRtransmission spectrum occur remain highly stable with respect totemperature for all water-based drugs (or other samples).

Importantly, due to the absence of temperature-sensitive narrow spectralabsorption features, they have established there is no requirement forhighly resolved spectral lines to be measured thereby eliminating theneed for an expensive monochromator. A small number of measurements (5or 6 typically) made at discrete wavelengths over the range 1300 nm to2000 nm is sufficient to characterise each drug (or other sample).Typically, each measurement is made over a bandwidth of 12 nm (asdetermined by a band-pass filter, illuminated by a broad-band source,for example) or over a few nanometers for laser-based illumination.

In general terms, a number of embodiments and variations are describedabove. It will be appreciated by those skilled in the art, combinationsof the features of the various embodiments could be envisaged and theembodiments described should not be considered limiting

The invention claimed is:
 1. An analyser for identifying or verifying orotherwise characterising a sample, the analyser comprising: anelectromagnetic radiation source for emitting electromagnetic radiationin at least one beam at a sample, the electromagnetic radiationcomprising at least two different wavelengths; a sample detector thatdetects affected electromagnetic radiation resulting from the emittedelectromagnetic radiation affected by the sample and provides outputrepresenting the detected affected radiation; a processor fordetermining sample coefficients from the output and identifying orverifying or otherwise characterising the sample using the samplecoefficients and training coefficients determined from training samples;and a temperature sensor to measure the temperature of the sample andprovide temperature output to the processor, wherein the processorcorrects spectral components of the training coefficients at the atleast two wavelengths based on the temperature of the sample.
 2. Ananalyser according to claim 1, wherein the sample detector outputrepresents intensities detected by the detector at the at least twowavelengths, and wherein determining the sample coefficients comprisesdeteimining and using a fractional spectral intensity at eachwavelength.
 3. An analyser according to claim 2, further comprising areference detector for detecting reference electromagnetic radiation atthe at least two wavelengths that provides output representingintensities detected at the at least two wavelengths, and the fractionalspectral intensity at each wavelength is a normalised fractionalspectral intensity using the output from the reference detector.
 4. Aanalyser according to claim 3, wherein the analyser is used on aplurality of training samples to obtain from the sample detectortraining output for a plurality of training samples representingintensities detected by the detector at the at least two wavelengths,and wherein the processor is configured to determine the trainingcoefficients by determining and using a fractional spectral intensity ateach wavelength of the training output.
 5. An analyser according toclaim 4, wherein the fractional spectral intensity is a normalisedfractional spectral intensity using output from a reference detector. 6.An analyser according to claim 5, wherein the fractional intensity isdefined as the proportion of transmitted light measured at a wavelengthreferenced to the sum of intensities over all the at least twowavelengths.
 7. An analyser according to claim 1, wherein thetemperature is corrected according to:${I( T_{t} )} = {{I( T_{b} )} + {\frac{dI}{dT}\Delta\; T}}$(which can be rearranged as I(T_(b))=I(T₁)−(dI/dT)ΔT) where I is theintensity of affected electromagnetic radiation detected by a detectorat a particular wavelength for a sample, T_(t) is the temperature of thetraining sample when the affected electromagnetic radiation was detectedat that wavelength, T_(b) is the temperature of the unknown sample whenthe affected electromagnetic radiation was detected at that wavelength,ΔT=T_(t)−T_(b) is the sample temperature difference between the trainingsample temperature and unknown sample temperature, I(T_(b)) is thetemperature corrected intensity, and $\frac{dI}{dT}$ is the slope of thelinear relationship of between measure intensity and temperature for asample at a given wavelength.
 8. A method for identifying or verifyingor otherwise characterising a sample, the method comprising: emittingelectromagnetic radiation from an electromagnetic radiation source in atleast one beam at a sample, the electromagnetic radiation comprising atleast two different wavelengths; using a sample detector, detectingaffected electromagnetic radiation resulting from the emittedelectromagnetic radiation affected by the sample and providing detectedoutput representing the detected affected radiation; using a processor,determining sample coefficients from the output; and using theprocessor, identifying or verifying or otherwise characterising thesample using the sample coefficients and training coefficientsdetermined from training samples, wherein a temperature of the sample ismeasured using a temperature sensor and used to correct spectralcomponents of the training coefficients at the at least two wavelengthsbased on the temperature of the sample.
 9. A method according to claim8, wherein the detected output represents intensities detected at the atleast two wavelengths, and wherein determining the sample coefficientscomprises determining and using a fractional spectral intensity at eachwavelength.
 10. A method according to claim 9, further comprisingdetecting, using a reference detector, reference electromagneticradiation at the at least two wavelengths and providing outputrepresenting intensities detected at the at least two wavelengths, andthe fractional spectral intensity at each wavelength is a normalisedfractional spectral intensity using the output from the referencedetector.
 11. A method according to claim 10, further comprising: for aplurality of training samples, emitting electromagnetic radiation fromthe electromagnetic radiation source or a second electromagneticradiation source in at least one beam at each training sample, theelectromagnetic radiation comprising at least two different wavelengths;for each training sample, using the sample detector or a seconddetector, detecting affected electromagnetic radiation resulting fromthe emitted electromagnetic radiation affected by the training sampleand providing detected output representing the detected affectedradiation; and for each training sample, using the processor or a secondprocessor, determining training coefficients from the output bydetermining and using a fractional spectral intensity at each wavelengthof the training output.
 12. A method according to claim 11, wherein thefractional intensity is defined as the proportion of transmitted lightmeasured at a wavelength referenced to the sum of intensities over allthe at least two wavelengths.
 13. A method according to claim 10,wherein the fractional spectral intensity is a normalised fractionalspectral intensity using output from a reference detector.
 14. A methodaccording to claim 8, wherein the temperature is corrected according to:${I( T_{t} )} = {{I( T_{b} )} + {\frac{dI}{dT}\Delta\; T}}$(which can be rearranged as I(T_(b))=I(T_(t))−(dI/dT)ΔT) where I is theintensity of affected electromagnetic radiation detected by a detectorat a particular wavelength for a sample, T_(t) is the temperature of thetraining sample when the affected electromagnetic radiation was detectedat that wavelength, T_(b) is the temperature of the unknown sample whenthe affected electromagnetic radiation was detected at that wavelength,ΔT=T_(t)−T_(b) is the sample temperature difference between the trainingsample temperature and unknown sample temperature, I(T_(b)) is thetemperature corrected intensity, and $\frac{dI}{dT}$ is the slope of thelinear relationship of between measure intensity and temperature for asample at a given wavelength.
 15. A method for identifying or verifyingor otherwise characterising a sample comprising: emittingelectromagnetic radiation from an electromagnetic radiation source in atleast one beam at a sample, the electromagnetic radiation comprising atleast two different wavelengths; using a first detector, detecting theemitted electromagnetic radiation at each wavelength and providingdetected output representing the emitted electromagnetic radiation beingreference intensity detected at each wavelength; using the firstdetector or a second detector, detecting affected electromagneticradiation resulting from the emitted electromagnetic radiation affectedby the sample and providing detected output representing the detectedaffected radiation being output intensity detected at each wavelength;measuring the temperature of the sample using a temperature sensor;using a processor, determining sample coefficients from the output; andusing the processor, identifying or verifying or otherwisecharacterising the sample using the sample coefficients and trainingcoefficients determined from training samples, wherein determining thesample coefficients comprises: using the processor, eliminating darkcurrent from the output of the reference and output intensities; usingthe processor, determining fractional spectral intensities from thereference and output intensities; using the processor, determining aconcentration independent coefficient from the fractional spectralintensities, wherein the training coefficients have been determined fromdata temperature corrected to the temperature of the sample.